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  • Volume 22, Issue 2
  • Integration of a theoretical framework into your research study
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  • Roberta Heale 1 ,
  • Helen Noble 2
  • 1 Laurentian University , School of Nursing , Sudbury , Ontario , Canada
  • 2 Queens University Belfast , School of Nursing and Midwifery , Belfast , UK
  • Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Ramsey Lake Road, Sudbury, P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/ebnurs-2019-103077

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Often the most difficult part of a research study is preparing the proposal based around a theoretical or philosophical framework. Graduate students ‘…express confusion, a lack of knowledge, and frustration with the challenge of choosing a theoretical framework and understanding how to apply it’. 1 However, the importance in understanding and applying a theoretical framework in research cannot be overestimated.

The choice of a theoretical framework for a research study is often a reflection of the researcher’s ontological (nature of being) and epistemological (theory of knowledge) perspective. We will not delve into these concepts, or personal philosophy in this article. Rather we will focus on how a theoretical framework can be integrated into research.

The theoretical framework is a blueprint for your research project 1 and serves several purposes. It informs the problem you have identified, the purpose and significance of your research demonstrating how your research fits with what is already known (relationship to existing theory and research). This provides a basis for your research questions, the literature review and the methodology and analysis that you choose. 1 Evidence of your chosen theoretical framework should be visible in every aspect of your research and should demonstrate the contribution of this research to knowledge. 2

What is a theory?

A theory is an explanation of a concept or an abstract idea of a phenomenon. An example of a theory is Bandura’s middle range theory of self-efficacy, 3 or the level of confidence one has in achieving a goal. Self-efficacy determines the coping behaviours that a person will exhibit when facing obstacles. Those who have high self-efficacy are likely to apply adequate effort leading to successful outcomes, while those with low self-efficacy are more likely to give up earlier and ultimately fail. Any research that is exploring concepts related to self-efficacy or the ability to manage difficult life situations might apply Bandura’s theoretical framework to their study.

Using a theoretical framework in a research study

Example 1: the big five theoretical framework.

The first example includes research which integrates the ‘Big Five’, a theoretical framework that includes concepts related to teamwork. These include team leadership, mutual performance monitoring, backup behaviour, adaptability and team orientation. 4 In order to conduct research incorporating a theoretical framework, the concepts need to be defined according to a frame of reference. This provides a means to understand the theoretical framework as it relates to a specific context and provides a mechanism for measurement of the concepts.

In this example, the concepts of the Big Five were given a conceptual definition, that provided a broad meaning and then an operational definition, which was more concrete. 4 From here, a survey was developed that reflected the operational definitions related to teamwork in nursing: the Nursing Teamwork Survey (NTS). 5 In this case, the concepts used in the theoretical framework, the Big Five, were the used to develop a survey specific to teamwork in nursing.

The NTS was used in research of nurses at one hospital in northeastern Ontario. Survey questions were grouped into subscales for analysis, that reflected the concepts of the Big Five. 6 For example, one finding of this study was that the nurses from the surgical unit rated the items in the subscale of ’team leadership' (one of the concepts in the Big Five) significantly lower than in the other units. The researchers looked back to the definition of this concept in the Big Five in their interpretation of the findings. Since the definition included a person(s) who has the leadership skills to facilitate teamwork among the nurses on the unit, the conclusion in this study was that the surgical unit lacked a mentor, or facilitator for teamwork. In this way, the theory of teamwork was presented through a set of concepts in a theoretical framework. The Theoretical Framework (TF)was the foundation for development of a survey related to a specific context, used to measure each of the concepts within the TF. Then, the analysis and results circled back to the concepts within the TF and provided a guide for the discussion and conclusions arising from the research.

Example 2: the Health Decisions Model

In another study which explored adherence to intravenous chemotherapy in African-American and Caucasian Women with early stage breast cancer, an adapted version of the Health Decisions Model (HDM) was used as the theoretical basis for the study. 7 The HDM, a revised version of the Health Belief Model, incorporates some aspects of the Health Belief Model and factors relating to patient preferences. 8 The HDM consists of six interrelated constituents that might predict how well a person adheres to a health decision. These include sociodemographic, social interaction, experience, knowledge, general and specific health beliefs and patient preferences, and are clearly defined. The HDM model was used to explore factors which might influence adherence to chemotherapy in women with breast cancer. Sociodemographic, social interaction, knowledge, personal experience and specific health beliefs were used as predictors of adherence to chemotherapy.

The findings were reported using the theoretical framework to discuss results. The study found that delay to treatment, health insurance, depression and symptom severity were predictors to starting chemotherapy which could potentially be adapted with clinical interventions. The findings from the study contribute to the existing body of literature related to cancer nursing.

Example 3: the nursing role effectiveness model

In this final example, research was conducted to determine the nursing processes that were associated with unexpected intensive care unit admissions. 9 The framework was the Nursing Role Effectiveness Model. In this theoretical framework, the concepts within Donabedian’s Quality Framework of Structure, Process and Outcome were each defined according to nursing practice. 10 11  Processes defined in the Nursing Role Effectiveness Model were used to identify the nursing process variables that were measured in the study.

A theoretical framework should be logically presented and represent the concepts, variables and relationships related to your research study, in order to clearly identify what will be examined, described or measured. It involves reading the literature and identifying a research question(s) while clearly defining and identifying the existing relationship between concepts and theories (related to your research questions[s] in the literature). You must then identify what you will examine or explore in relation to the concepts of the theoretical framework. Once you present your findings using the theoretical framework you will be able to articulate how your study relates to and may potentially advance your chosen theory and add to knowledge.

  • Kalisch BJ ,
  • Parent M , et al
  • Strickland OL ,
  • Dalton JA , et al
  • Eraker SA ,
  • Kirscht JP ,
  • Lightfoot N , et al
  • Harrison MB ,
  • Laschinger H , et al

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Patient and public involvement Not required.

Read the full text or download the PDF:

Who uses nursing theory? A univariate descriptive analysis of five years' research articles

Affiliation.

  • 1 Nursing PhD Program, University of Jordan, Amman, Jordan. [email protected]
  • PMID: 20950408
  • DOI: 10.1111/j.1471-6712.2010.00835.x

Background: Since the early 1950s, nursing leaders have worked diligently to build the Scientific Discipline of Nursing, integrating Theory, Research and Practice. Recently, the role of theory has again come into question, with some scientists claiming nurses are not using theory to guide their research, with which to improve practice.

Aims: The purposes of this descriptive study were to determine: (i) Were nursing scientists' research articles in leading nursing journals based on theory? (ii) If so, were the theories nursing theories or borrowed theories? (iii) Were the theories integrated into the studies, or were they used as organizing frameworks?

Methods: Research articles from seven top ISI journals were analysed, excluding regularly featured columns, meta-analyses, secondary analysis, case studies and literature reviews. The authors used King's dynamic Interacting system and Goal Attainment Theory as an organizing framework. They developed consensus on how to identify the integration of theory, searching the Title, Abstract, Aims, Methods, Discussion and Conclusion sections of each research article, whether quantitative or qualitative.

Results: Of 2857 articles published in the seven journals from 2002 to, and including, 2006, 2184 (76%) were research articles. Of the 837 (38%) authors who used theories, 460 (55%) used nursing theories, 377 (45%) used other theories: 776 (93%) of those who used theory integrated it into their studies, including qualitative studies, while 51 (7%) reported they used theory as an organizing framework for their studies. Closer analysis revealed theory principles were implicitly implied, even in research reports that did not explicitly report theory usage.

Conclusions: Increasing numbers of nursing research articles (though not percentagewise) continue to be guided by theory, and not always by nursing theory. Newer nursing research methods may not explicitly state the use of nursing theory, though it is implicitly implied.

© 2010 The Authors. Scandinavian Journal of Caring Sciences © 2010 Nordic College of Caring Science.

  • Nursing Research
  • Nursing Theory*
  • Open access
  • Published: 19 March 2024

Unfinished nursing care in healthcare settings during the COVID-19 pandemic: a systematic review

  • Aysun Bayram   ORCID: orcid.org/0000-0003-2038-6265 1 ,
  • Stefania Chiappinotto   ORCID: orcid.org/0000-0003-4829-1831 2 &
  • Alvisa Palese   ORCID: orcid.org/0000-0002-3508-844X 2  

BMC Health Services Research volume  24 , Article number:  352 ( 2024 ) Cite this article

Metrics details

Unfinished nursing care is becoming increasingly more of a concern in worldwide healthcare settings. Given their negative outcomes, it is crucial to continuously assess those nursing interventions that are commonly postponed or missed, as well as the underlying reasons and consequences. The worldwide COVID-19 pandemic has made it difficult for health facilities to maintain their sustainability and continuity of care, which has also influenced the unfinished nursing care phenomenon. However, no summary of the studies conducted during the COVID-19 pandemic was produced up to now. The main aim of this study was to systematically review the occurrence of, reasons for, and consequences of unfinished nursing care among patients in healthcare settings during the COVID-19 pandemic.

Systematic review registered in PROSPERO (CRD42023422871). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guideline and the Joanna Briggs Institute Critical Appraisal tool for cross-sectional studies were used. MEDLINE-PubMed, the Cumulative Index to Nursing and Allied Health Literature, and Scopus were searched from March 2020 up to May 2023, using keywords established in the field as missed care, unfinished nursing care, or implicit rationing.

Twenty-five studies conducted mainly in European and Asiatic countries were included and assessed as possessing good methodological quality. The following tools were used: the MISSCARE Survey (= 14); the Basel Extent of Rationing of Nursing Care (= 1), also in its revised form (= 2) and regarding nursing homes (= 2); the Perceived Implicit Rationing of Nursing Care (= 4); the Intensive Care Unit-Omitted Nursing Care (= 1); and the Unfinished Nursing Care Survey (= 1). The order of unfinished nursing care interventions that emerged across studies for some countries is substantially in line with pre-pandemic data (e.g., oral care, ambulation). However, some interesting variations emerged at the country and inter-country levels. Conversely, labour resources and reasons close to the emotional state and well-being of nurses were mentioned homogeneously as most affecting unfinished nursing care during the pandemic. None of the studies investigated the consequences of unfinished nursing care.

Conclusions

Two continents led the research in this field during the pandemic: Europe, where this research was already well established, and Asia, where this research is substantially new. While unfinished care occurrence seems to be based on pre-established patterns across Europe (e.g., regarding fundamentals needs), new patterns emerged across Asiatic countries. Among the reasons, homogeneity in the findings emerged all in line with those documented in the pre-pandemic era.

Peer Review reports

Unfinished nursing care (UNC), which is becoming increasingly more of a concern in worldwide healthcare settings, involves the skipped, delayed, or incomplete delivery of nursing interventions needed for the patient and/or the patient’s family [ 1 , 2 ]. The prevalence of UNC, which ranges from 55 to 98% globally [ 1 ], is considered as an accurate indicator of both patient safety and nursing care quality [ 3 , 4 ]. The primary reasons for UNC are issues in communication, labour, and material resources [ 5 ]. The occurrence of UNC has also been associated with staff shortage and factors at both the structural level (e.g., nurses’ roles and experiences) and the process level, such as the stressful work environment, some negative managerial practices, the amount of overtime, and the high and/or complex demand for patient care [ 6 , 7 , 8 , 9 , 10 , 11 ]. In terms of consequences, UNC is linked to poor patient (e.g., pressure sores), nurse (e.g., moral distress), and organisational outcomes (e.g., increased length of stay) [ 5 , 12 , 13 , 14 ]. Given these unfavourable outcomes, it is crucial to continuously assess those nursing interventions that are commonly postponed or missed, as well as the underlying reasons and consequences, to inform evidence-based strategies aimed at decreasing the frequency of UNC.

The worldwide COVID-19 pandemic has made it difficult for health facilities to maintain their sustainability and continuity of care due to the dramatic call to increase the care capacity with limited resources [ 15 , 16 , 17 ]. The staff sector most impacted by the pandemic — especially due to concerns regarding infection — has been recognised as nursing staff delivering direct patient care and thus representing the most crucial element of the health system infrastructure [ 18 ]. In addition to the need to increase the amount of care, nurses have also been impacted by unfamiliar work settings due to changes in the layout of the hospitals, sickness exposure, and urgent deployment from one department to another without the required skills. Therefore, various components (e.g., communication) of nursing care have been compromised by the limited interaction required during the pandemic and the need to be distanced. Nurses’ care capacity has also been negatively impacted by feelings related to the pandemic triggering anxiety, depression, and burnout [ 19 , 20 ]. A rise in the number of nurses layoffs, the increased shortage of nurses, poor working circumstances, negative feelings, and imbalances in the nurse–patient ratio may all have increased the occurrence of UNC during the pandemic [ 21 , 22 ] by further eroding the quality of care [ 23 , 24 ]. Gurkovà et al. [ 25 ] stated that UNC may have increased the risk and adverse effects of the COVID-19 pandemic, resulting in ethical issues and a widespread mistrust in health systems [ 26 ]; moreover, Nash et al. [ 27 ] also stated that healthcare disparities were the consequences of UNC.

However, while the pre-pandemic occurrence of UNC has been well established, with several primary studies and systematic reviews (e.g., [ 28 ]) also investigating the underlying reasons (e.g., [ 29 ]), no summary of the studies conducted during the pandemic has been provided to date. Summarising the evidence produced may highlight the issues experienced during the pandemic in order to prevent them in future epidemiological disasters. It may also provide information on the quality of care in dramatic circumstances and the variations, if any, in the routine care before the pandemic. Finally, it may also set a new baseline in the context of UNC given the profound disruption and changes affecting the healthcare systems, requiring a long-term recovery. Thus, the aim of this review was to systematically review the occurrence of, reasons for, and consequences of UNC among patients in healthcare settings in the face of the COVID-19 pandemic.

To begin with, two researchers (AB, SC) performed a rapid literature search to establish whether any studies had been published on UNC occurrences, their reasons, and consequences among patients during the pandemic. The beginning of the pandemic period was defined as 11 March 2020, according to the declaration by the World Health Organisation [ 30 ].

According to the Population (P), Exposure (E), Comparator (C), Outcomes (O), and Study Design (S) framework [ 31 ], the following were considered: P, patients in any healthcare setting; E, the COVID-19 pandemic period, as started on 11 March 2020 up to 5 May 2023 [ 30 ]; C, none; O, occurrence, reasons, and consequences of UNC, as perceived by nursing staff; and S, any types of quantitative study designs. Consequently, the following research questions were identified: (1) What was the occurrence of the UNC phenomenon among patients during the pandemic? (2) What were the reasons for the UNC during the pandemic? (3) What were the consequences of the UNC among patients during the pandemic? (4) What were the main methodological features of studies designed/conducted during the pandemic?

The systematic review was reported in its methods and findings according to Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) guidelines [ 32 ].

Ethical considerations

The researchers designed a systematic review protocol that was registered in PROSPERO (CRD42023422871).

Inclusion and exclusion criteria

Studies were considered if they (1) regarded the nursing field; (2) focused on UNC occurrence, its reasons, and/or consequences during the pandemic, as perceived by nurses and nursing aides; (3) were published in English, Italian, or Turkish; (4) collected the data using a validated tool/instrument in the UNC field; (5) were conducted after 11 March 2020 during the COVID-19 pandemic up to 5 May 2023 [ 30 ]; and (6) used any types of quantitative designs (randomised controlled trials, non-randomised controlled trials, cohort studies, prospective or retrospective observational studies, cross-sectional studies, longitudinal studies).

Studies were excluded if they (1) did not address UNC data and/or did not involve nurses/nursing aides or care workers in the nursing field; (2) used non-validated tools/instruments measuring UNC or interviews; (3) were conducted in a paediatric setting, due to its specificity not being comparable with the adult field; (4) were designed as qualitative studies, reviews, commentaries, editorials, or books; (5) were written in other languages; or (6) had an abstract/full text that was not accessible.

Search method

MEDLINE-PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Scopus were searched to identify the eligible studies as sources on 5 May 2023. According to the uniqueness of this research, where no MeSH terms have been established and different key words are used [ 1 , 2 ], all synonymous and equivalent keywords established in the field of UNC were used to access the databases. Specifically, the following keywords were used: “nurse”, “nursing”, “missed care”, “missed nursing care”, “unfinished nursing care”, “unfinished care”, “implicit rationing of nursing care”, “implicit rationing”, “rationing of nursing care”, “rationed care”, “prioritization process”, “omitted nursing care”, “task left undone”, and “task undone” using “OR” and “AND” operators (Supplementary Table 1 ).

Quality Appraisal

The Joanna Briggs Quality Appraisal Tool for analytical cross-sectional studies was used in the quality assessment for all eligible studies when they were based on cross-sectional designs [ 33 ]. This tool contains eight items with response options of yes, no, unclear, and not applicable. These items regarded inclusion criteria, subjects and setting description, exposure, standard criteria for measurement of the condition, confounding factors, strategies to deal with confounding factors, outcomes measurement, and statistical analysis. Two researchers (AB, SC) independently assessed the quality of the studies as “Rater 1” and “Rater 2”. In the case of a disagreement, the senior researcher (AP) was consulted to reach a consensus, as summarised analytically in Supplementary Table 2 .

Besides the quality appraisal, to prevent bias, the following strategies were applied: (a) all researchers contributed to the writing of the review protocol; (b) at least two researchers searched the literature, chose the studies, and extracted the data, independently; (c) the senior researcher oversaw the data extraction; and (d) agreement was required before moving on to each next step.

Data extraction and synthesis

All studies that met the inclusion criteria, regardless of the results of their methodological quality, underwent the data extraction and data synthesis. The studies were divided into two groups and shared between two researchers (AB, SC). In primis , the data extraction grid was piloted in one study, and the findings agreed: no changes were required. Then, researchers independently extracted data from the remaining studies by populating the grid with the following data: (1) author(s), year, and country; (2) study aim(s) and design; (3) sample and setting; and (4) period of data collection and tool(s). Then the findings of the quality appraisal were provided (Table  1 ). At the end of data extraction, the researchers rechecked the data. Disagreements were solved with the consultation of the senior researcher (AP) until consensus was reached.

A narrative synthesis process was used to summarise the findings [ 57 ] according to the review questions, applying the following methodology:

Studies conducted during the pandemic and their methodological quality: the researchers conducted a preliminary synthesis to provide an initial description of the main characteristics of the studies and their methodological quality, and similarities and differences across studies were presented by using textual explanations [ 57 ].

The occurrence of UNC: Findings were tabulated according to the tools used in each study, namely the MISSCARE Survey, the Basel Extent of Rationing of Nursing Care (BERNCA) and the Revised BERNCA (BERNCA-R), the Perceived Implicit Rationing of Nursing Care (PIRNCA), the Basel Extent of Rationing of Nursing Care for Nursing Homes (BERNCA-NH), the Intensive Care Unit Omitted Nursing Care instrument (ICU-ONC), and the Unfinished Nursing Care Survey (UNCS). In all tools, participants are required to rank the nursing interventions missed and/or postponed from always to never. Then, according to the following considerations,

the tools used different metrics (Likert from 1 to 5 for MISSCARE Survey and UNCS, from 0 to 4 for BERNCA, from 0 to 3 for PIRNCA, from 1 to 4 for BERNCA-NH, from 1 to 4 for ICU-ONC) and differed in the direction of measures (e.g., from always missed to never missed, e.g., [ 43 ], or the opposite, e.g. [ 50 ]); and

UNC interventions reflect an order [ 58 , 59 ], such as first, second, and third, of interventions missed, expressing a prioritisation process (what should be actualised first and what can be delayed).

Data regarding the position (= order) of each nursing intervention according to the averages documented in the studies were extracted and then ranked according to the position: for example, the average of 3.23 with the MISSCARE Survey [ 35 ], indicating that this was the most unfinished activity, was ranked as first. Then, according to Blackman and colleagues [ 60 ], the first three interventions of high occurrence of being unfinished were identified; from the fourth to the sixth, those of intermediate occurrence; and from the seventh to ninth, those of a low occurrence of UNC.

The UNC reasons: Reasons were summarised based on the following considerations:

Studies using the MISSCARE Survey and the UNCS reported the reasons for UNC item by item, according to the structure of the tool;

Other studies documented the relationships (as correlations, associations) indicating a significant role of some factors in increasing/hindering UNC during the pandemic.

In the first case, the reasons were extracted and analysed in the same manner as UNC activities; in the second, studies (22 out of 25) documenting a statistically significant relationship of given factors with the UNC were extracted and categorised as organisational, work, or individual factors according to the literature in the field [ 29 ]. Of the remaining three studies, which were not focused on the reason for UNC, one was a methodological study that was focused on the psychometric assessment of the tool [ 56 ], one was a comparative study that was focused on the comparison between the data from a COVID-19 sample and a reference sample [ 54 ], and one was a study in which conditions were identified affected by the consequences of UNC [ 48 ].

UNC main consequences: if any, were described narratively.

All researchers were involved in the data analysis and synthesis process to ensure rigour in the process.

The results regarding the included studies are described below, including an exploration of their characteristics and quality and the occurrence of, reasons for, and consequences of UNC.

Search outcomes

In total, 1,389 articles were identified from the electronic databases. The search results were transferred to a reference manager (Mendeley) to organise the data extraction process. First, three steps were followed for the study selection: in the first stage, titles, in the second stage, abstracts, and in the third stage full text of the retrieved studies were screened for their eligibility by two reviewers (AB, SC), independently. In the case of any disagreement, the opinions of a third senior researcher (AP) were consulted during the entire process. Consensus between the researchers was essential for study inclusion.

In the first stage, 726 studies were excluded; from 1,389 studies, 663 articles were retained for abstract screening. Thus, in the second stage, 298 studies were excluded. At this stage, 365 studies met the criteria for next-step screening. Before the full-text screening, 219 duplicated studies were removed, and a visual inspection was conducted by two researchers (AB, SC) to check for duplicates. Then, 146 studies remained for full-text screening, and 122 of them were excluded for different reasons, as reported in Fig.  1 . The references of the excluded reviews were screened by two researchers (AB, SC) to check their eligibility in an independent fashion and then agreed upon. In total, 38 articles were checked, of which 33 were already included, three were not related to UNC, and one was a qualitative study design. At the end of the screening process, 25 studies were included (Fig.  1 ).

figure 1

PRISMA flow chart

Included studies and their quality

Out of the 25 studies included (Table  1 ), 20 used a descriptive cross-sectional design (e.g., [ 34 ]) and five a comparative cross-sectional design confronting the data (a) before and during the pandemic [ 35 ]; (b) or before the pandemic, and the second/third wave [ 38 ]; and (c) of the COVID-19 sample and the reference sample [ 37 , 48 , 54 ]. Most studies were conducted in Europe (= 12, e.g., [ 50 ]) and Asia (= 11, e.g., [ 45 ]). Of the remaining, one was carried out in Africa [ 47 ] and one in Canada [ 53 ]. Study locations ranged from a hospital (e.g., [ 35 ]) to specific hospital settings (tertiary [ 55 ], district [ 51 ], government [ 56 ], private [ 34 ], teaching [ 50 ]) in various types of units (e.g., medical/surgical [ 54 ], urology [ 43 ], cardiology [ 48 ]). In addition, COVID-19 units were included in two studies [ 22 , 37 , 41 ] and nursing homes in another two [ 22 , 40 ].

Studies were published between 2020 and 2023; however, nine of them completed the data collection in 2020 (e.g., [ 52 ]), 10 in 2021 (e.g., [ 47 ]), two between 2020 and 2021 [ 37 , 50 ], one in 2022 [ 55 ], two between 2019 and 2020 [ 35 , 54 ], and one between 2019 and 2021 [ 38 ]. Participants were mainly nurses, and their sample size ranged from 130 [ 42 ] to 672 [ 34 ] in 21 studies; in others, participants were generally identified as “care workers”, ranging from 374 [ 22 ] to 2,700 [ 40 ], while those including nursing assistants and registered nurses together ranged from 43 [ 48 ] to 287 [ 54 ] participants. The MISSCARE Survey tool was the most used (= 14, e.g., [ 44 ]), followed by BERNCA (= 1, [ 46 ]), Revised BERNCA (BERNCA-R) (= 2, [ 51 , 52 ]), BERNCA-NH (= 2, [ 22 , 40 ]), PIRNCA (= 4, e.g., [ 42 ]), ICU-ONC (= 1, [ 53 ]), and UNCS (= 1, [ 37 ]) (Table  1 ).

All studies reported a good methodological quality with minimal bias (Supplementary Table 2 ). Most were ranked positively for at least six (“yes” responses) out of eight questions (= 11; e.g., [ 39 ]), nine studies for at least seven questions (e.g., [ 44 ]), and five for at least five questions (e.g., [ 41 ]). Four studies failed to clarify the strategies to deal with confounding factors (e.g., [ 56 ]), while seven described these strategies unclearly (e.g., [ 51 ]). The settings and study subjects were stated as being unclear in eight studies (e.g., [ 52 ]). Additionally, in one study, the sample inclusion criteria were not detailed, while in another study, the confounding factors were not reported. The objective, standard criteria used to measure the condition were not assessable in any of the qualified studies, since the condition was considered the COVID-19 disease. At the overall level, all except six studies [ 25 , 34 , 42 , 43 , 46 , 55 ] documented the occurrence of and reasons for UNC activities.

The occurrence of UNC

In the 14 studies based on the MISSCARE survey, the most frequent UNC activities were “Ambulation 3 times per day or as ordered”, “Turning patient every two hours”, “Attending interdisciplinary care conferences whenever held”, “Providing mouth care”, and “Patient teaching about procedures, tests and other diagnostic studies”. In particular, “Ambulation 3 times per day or as ordered” was the activity most missed in three studies [ 35 , 38 , 39 ]; it was the second unfinished activity in the study by Al Muharraq et al. [ 36 ] and the third in another three studies ([ 48 ]; in both the COVID-19 sample and the reference sample of von Vogelsang et al. [ 54 ]) (Table  2 , Supplementary Table 3 ). “Turning patient every two hours” was the most frequent UNC activity in two studies (in the COVID-19 sample of Nymark et al. [ 48 ]; in the reference sample of von Vogelsang et al. [ 54 ]) and the second in another three ([ 35 ]; in the reference sample of Nymark et al. [ 48 ]; in the third wave sample of Falk et al. [ 38 ]). This activity was third in another four studies ([ 35 , 36 , 38 ]; second wave [ 47 ]) (Table  2 , Supplementary Table 3 ). However, the first unfinished activity in five studies was “Attending interdisciplinary care conferences whenever held” ([ 36 , 44 , 49 ]; in the reference sample of Nymark et al. [ 48 ]; in the COVID-19 sample of von Vogelsang et al. [ 54 ]) and “Monitoring patient” in one study [ 45 ] (Table  2 , Supplementary Table 3 ). Conversely, the least frequently unfinished activities were “Monitoring intake/output”, “Vital signs assessed as ordered”, “Bedside glucose monitoring”, and “Patient assessments every shift” (Table  2 , Supplementary Table 3 ).

Considering the studies using the PIRNCA tool, the most frequent unfinished interventions were the “Coordination of care and discharge planning” and the least common the “Implementation of prescribed treatment plan” in Schneider-Matyka et al. [ 50 ]. Contrarily, Yuwanto et al. [ 56 ] discovered that “Coordination of care and discharge planning” were the least frequently unfinished activities. The other most frequent UNC activities were listed in Schneider-Matyka et al. [ 50 ] and Yuwanto et al. [ 56 ], respectively, as (i) “Offer emotional or psychological support”, (ii) “Converse with team members”, (iii) “Converse with external agency”, and (i) “Routine skin care”, (ii) “Converse with external agency”, and (iii) “Assist with bowel and bladder elimination”, while the least unfinished were, respectively, (i) “Medication administration”, (ii) “Enteral and parenteral nutrition”, and (i) “Converse with patient regarding discharge”, (ii) “Infection control practices” (Table  3 , Supplementary Table 4 ).

In accordance with Tomaszewska et al. [ 51 ] and Uchmanowicz et al. [ 52 ], who used BERNCA-R, the most common first, second, and third UNC activities were “Education and training”, “Necessary disinfection measures”, and “Monitoring patients as the nurse felt necessary”. The studies identified “Change of the bed linen”, “Skin care”, and “Assist food intake” as the least frequent UNC activities [ 51 ] (Table  4 , Supplementary Table 5 ).

In two studies that used the BERNCA-NH tool, “Social care” and “Emotional support” reported the highest occurrences [ 22 , 40 ]. The most frequent UNC activities were listed in Hackman et al. [ 40 ] as (i) “Cultural activity for residents with contact outside of nursing home”, (ii) “Scheduled single activity with a resident”, and (i) “Scheduled group activity with several residents”; in contrast, the most frequent unfinished activities in Zhang et al. [ 22 ] were (i) “Activating or rehabilitating care”, (ii) “Emotional support”, and (iii) “Scheduled group activity with several residents”. On the other hand, “Assist dressing/undressing”, “Drinking”, “Food intake”, and “Sponge bath/partial sponge bath/skin care” were listed as the least frequent UNC activities [ 22 , 40 ] (Table  4 , Supplementary Table 5 ).

In the remaining two studies, recent tools were used. In the study conducted using the ICU-ONC tool, the most common unfinished activities were “Mobilization every two hours”, “Mouth care for intubated patients”, and “Document treatments and procedures”; those least frequent were “Cardiac monitoring surveillance”, “Flag the presence of signs or symptoms of infection”, and “Titrate intravenous perfusions for hemodynamic targets” [ 53 ] (Table  5 , Supplementary Table 6 ). In the study using the UNCS [ 37 ], the most frequent UNC for both the COVID-19 sample and the reference sample were “Performing bedside glucose monitoring as prescribed”, “Performing clinical handover to adequately inform the next shift nursing team about patients’ conditions”, and “Recording vital signs as planned”, while the least frequently unfinished activities were “Helping patient in need in ambulation”, “Providing passive mobilization/changing position in bedrest patient”, and “Providing mouth care to patients who need it” (Table  6 , Supplementary Table 7 ).

The reasons for UNC

Among the studies using the MISSCARE Survey, four [ 39 , 45 , 49 , 55 ] did not report the reasons item by item. In the remaining, “Inadequate number of staff” (e.g., in Wave 1 and Wave 2 sample of Falk et al. [ 38 ]; [ 25 ]) was reported as the most significant reason in six studies, “Unexpected raise in patient volume and/or acuity” as the first or second reason in four studies (e.g., [ 38 , 48 ]), and “Urgent patient situations” as the first, second, or third in six studies (e.g., [ 41 , 47 ]) (Table  2 , Supplementary Table 3 ). The reasons for UNC that were given least were “Other departments did not provide the needed care”, “Inadequate hand-off from previous shift or sending unit”, “Caregiver is off unit or unavailable”, and “Tension or communication breakdowns with the medical staff/other support departments” (Table  2 , Supplementary Table 3 ).

Regarding the findings from the UNCS [ 37 ], “Priority setting” and “Supervision of nursing aides” were reported as the most frequent factors causing UNC, followed by “Communication”. In particular, the most frequent reasons were “Inaccurate initial priority setting”, “Tension/conflicts within the nursing staff”, and “Inadequate nursing care model (e.g., functional task-oriented model of care)”. The reasons given least were the material and human resources as well as the unpredictability of the workflows (Table  6 , Supplementary File 7 ).

In 22 studies, UNC has been linked to other, additional factors. Among these, organisational factors, insufficient resources, and large hospital facilities were reported as increasing UNC [ 40 , 45 ]; other factors (e.g., adequate staff, the quality of care, the safety of the patients in the unit, a favourable nursing work environment, and the perceived accountability, organisational support, and leadership) hindered the occurrence of UNC (Table  7 ). Among the work-related factors, the type of shift work (afternoon shift [ 35 ]; 12-hour shift [ 41 ]; both day and night shift (not only night shift) [ 47 ]), overtime work, the type of unit, the workloads, and other factors increased the occurrence of UNC, whereas having a few patients to each nurse or COVID-19 patients, or better staffing levels, all decreased the occurrence of UNC (Table  7 ). Moreover, at the individual level, less than 10 years of experience and several other factors close to the nurses’ emotional state and well-being all decreased the occurrence of UNC (Table  7 ).

The Main consequences of UNC

No studies reported the consequences of UNC.

At the overall level, a total of 25 studies conducted mainly in European and Asiatic countries were produced during the pandemic, around 10 studies a year, continuing the tradition of this research field during difficult times for both nurses and healthcare settings. All tools available in the field were used, mostly the MISSCARE Survey, but also, on fewer occasions, BERNCA, also in its revised forms. As previously, mostly cross-sectional studies along with a few comparative studies were produced, suggesting the likelihood of a merely descriptive intent due to the challenging times. The order of UNC interventions that emerged across studies is substantially in line with pre-pandemic data, while some interesting variations emerged at the country and inter-country levels. Labour resources and reasons close to the emotional state and well-being of nurses were mentioned as most affecting UNC during the pandemic. However, none of the studies investigated the consequences of the phenomenon.

The discussion section follows the results structure and includes a reflection on the methodological quality of the studies and UNC occurrence, reasons, and consequences.

Included studies and their methodological quality

Studies released after the World Health Organisation declared the COVID-19 pandemic [ 30 ] as a period characterised by altered working conditions, workloads, and processes compared to those of the pre-pandemic era were included. No UNC differences between COVID-19 and non-COVID-19 patients emerged [ 63 , 64 ], suggesting that the pandemic affected the whole system. Moreover, given the substantial disruption of the routine care processes in the health systems, which may require time to recover, and with the likelihood of not reaching the same levels of the pre-pandemic era, a comprehensive review may contribute to providing a new reference point for future studies in the field of UNC.

Fewer than 10 studies a year were produced, in line with the pre-pandemic era [ 64 , 65 ]; moreover, data collection was performed mainly in 2020 and 2021, suggesting that available findings reflect the first phases of the pandemic. The leading continents in these studies were Europe and Asia, unlike in the past when the United States was the leading country, given that the missed care/left undone concepts were developed there [ 2 ]. Asian and European countries were those firstly and dramatically hit by the pandemic, thus triggering researchers to measure the UNC. However, the setting of the data collection has remained the hospital, as in the pre-pandemic era [ 66 ]: this finding is in line with the expanded capacity required in the hospitals and the recognition of their key role, especially in some waves, in facing the pandemic. Interestingly, several studies involved more units in very different institutions (e.g., [ 47 ]), which seems to suggest that this research line was scaled up during the pandemic from unit-based studies to large healthcare systems, thus embodying a reasonable health service research perspective because the whole system was changed to provide the care, and no one single part was left unaltered.

The study designs were cross-sectional with some comparative examples, as documented in the pre-pandemic era (e.g., [ 29 ]). The turbulent environments may have prevented longitudinal studies (e.g., to discover UNC outcomes). Forty-three [ 48 ] to 2,700 [ 40 ] nurses, nursing assistants, and care workers were involved, the sample sizes mirroring those of the pre-pandemic era [ 66 ]. However, no studies involved midwives, which suggests a lack of evidence in terms of what happened in maternal and paediatric departments.

Four different tools have been used to measure UNC, from those most validated across the world, namely the MISSCARE Survey [ 39 ] to more recent instruments, such as the ICU-ONC [ 53 ]. The different instruments used reflect the trends in this research field, characterised by a range of validated tools, thus preventing comparisons across studies. On the one hand, the utilisation of classic, well-validated tools may have provided accurate data and increased the comparison with pre- and intra-pandemic studies, whereas on the other hand, tools designed for a non-pandemic situation may have failed in their capacity to detect UNC in extraordinary conditions. Moreover, all tools collected UNC data as perceived by nurses, and their perceptions may have been influenced by the stress and the dramatic working conditions they were experiencing, as well as by the desire to do the best for the patients.

The overall quality of the studies was methodologically good: the extraordinary difficulties posed by the pandemic also required new strategies (e.g., to promote study participation among nurses, design protocols, and initiate studies while other priorities are perceived) in conducting research and seem to have been faced appropriately by researchers.

The different UNC activities, in their order, can be discussed around three main perspectives: (1) the instrument used; (2) the intercountry and intra-countries differences; and (3) the state of the evidence in the pre-pandemic era. The order of UNC interventions emerged across studies, for some countries are substantially in line with pre-pandemic data. The MISSCARE Survey studies highlighted that, during the pandemic, nurses firstly postponed or omitted interventions that call for proximity to the patient, such as oral care, or one-on-one interaction, such as ambulation. Studies using the ICU-ONC tool also showed the same trend, suggesting that these two tools can detect actions of care at the bedside. Nursing interventions related to organisation and communication were instead commonly unfinished in studies using the PIRNCA scale. Communication should also be seen as a fundamental care [ 67 , 68 , 69 ], as speaking and listening were most often seen as a nursing necessity during the pandemic. Differently, education, disinfection measures, and monitoring were the most frequent UNC activities in studies employing the BERNCA scale. Likewise, nursing interventions for patient follow-up were frequently unfinished in a study using the UNCS [ 37 ].

The most significant nursing interventions identified during the pandemic were monitoring, educating the patient, and implementing preventive measures against infections. Nurses may have felt that their usual applications were inadequate or incomplete given the growing demand for these interventions, or they may have believed that they would be unable to complete these applications out of fear of failing. Finally, social and rehabilitative nursing interventions were ranked first as unfinished activities in studies using the BERNCA-NH instrument. This reflects the contingencies of the COVID-19 pandemic, which forced residents of nursing homes to remain in their own rooms [ 70 ]. Therefore, at the overall level, it seems that nurses adopted the pre-pandemic patterns of prioritisation (e.g., failing in ensuring fundamental care) with the intent of reducing exposure in patients’ rooms for an extended period and to avoid the source of contagion [ 71 ], and/or due to the fatigue caused by the personal protective equipment worn (e.g., [ 72 ]). The rationed nursing activities did not turn out to be very different from those of the pre-pandemic period (e.g., [ 2 , 73 ]), as also emerged in those studies that included comparative studies [ 35 , 38 ].

However, interesting intra- and inter-country differences have emerged: at the intra-country level, two main patterns are evident. In Sweden, for example, Falk et al. [ 38 ] and von Vogelsan et al. [ 54 ] found that the three most unfinished activities are substantially the same, whereas in Jordan [ 35 , 44 ] and Iran [ 41 , 49 ], the first three unfinished activities differ (Table  2 , Supplementary Table 3 ). Similarly, at the inter-country level, in those studies using the MISSCARE Survey performed across Europe, the unfinished activities seem to have similar trends in the order pattern. Comparing these countries with those where UNC has started to be measured (e.g., Iran, Jordan, Saudia Arabia, Indonesia, Sultanate of Oman), feeding the patient and offering emotional support were not missed immediately, while attending interdisciplinary meetings was unfinished at first. In the two studies using the BERNCA-NH tool, a similar divergence appeared: in the study by Zhang et al. [ 22 ] performed in China, some activities (i.e., providing emotional support and rehabilitation care) were the first to be unfinished, while in Hackman et al. [ 40 ] these were ranked as being missed less often. Examples can also be found in studies using the PIRNCA and performed in Poland [ 50 ] and Indonesia [ 56 ]. On the one hand, this seems to suggest that when the healthcare system is under tremendous pressure, as during the pandemic, the process of prioritisation is based on pre-established patterns (e.g., across Europe; [ 74 ]); on the other hand, different patterns seem to be enacted outside of Europe, mainly in Asiatic countries. Given that these countries are substantially new to measuring UNC, replicating studies to establish whether the emerged patterns are the same as those used in normal conditions is strongly recommended.

Above all, studies produced during the pandemic period report unfinished activities according to the tool used. For example, the MISSCARE Survey was developed in the early 2000s [ 59 ] and is able to measure “basic” nursing activities; therefore, its capacity to detect exactly what happened in the nursing processes during the pandemic should be debated.

First, issues regarding human resources and the increased needs of patients were the most cited reasons in those studies using the MISSCARE tool, while issues among the staff or across departments impacted only a little. This is likely derived from the expanded capacity of the health systems under urgent circumstances [ 75 ] that increased the well-known shortages in resources, whereas facing the pandemic reduced tensions within the staff and across units, promoting a sense of collaboration [ 76 , 77 ]. Moreover, nurses became infected and were not available when quarantined: all these situations seriously disrupted the capacity of nursing care [ 21 , 22 ], threatening the patients’ needs [ 16 , 17 , 78 ]. Conversely, for Cengia et al. [ 37 ], human resources were not an issue in triggering UNC occurrence; however, this is a single study with the UNC survey tool, and although performed in several facilities, its findings may be interpreted from different perspectives: the units involved in the study may have been better equipped during the pandemic to deal with the situation, or nurses may have learnt for several years how to work under pressure, with limited resources, in a sort of “normalised” condition, where working under such conditions was not an issue [ 63 ].

Other potential reasons documented among studies are in line with those documented by Chiappinotto et al. [ 29 ]. However, two new elements emerged at the overall level among studies performed during the pandemic. Firstly, in those cases where the same reason has been documented (e.g., the role of working overtime [ 25 , 39 , 47 ]), no conflicting findings have been reported across studies, suggesting an evident accumulation of knowledge in the same direction. Previously, conflicting findings emerged for the same reasons across studies, in some increasing and in others hindering the occurrence of UNC (e.g., working overtime [ 29 ]). The increased homogeneity of the findings that emerged in the pandemic studies may depend on the same circumstances experienced in all healthcare services across the world. Secondly, several emotional factors at the nurses’ level (e.g., satisfaction, burnout, satisfaction with economic situation, stress) have been investigated and associated with UNC. The focus seems to be the professional and personal well-being of the nurses, reasons that may have a role as antecedents of UNC but that also express the consequences of the unfinished care phenomenon itself as well as the consequences of the exacerbated working conditions during the pandemic.

No UNC consequences have been documented to date confirming the tradition of this research field in which outcomes are under-reported [ 79 ]. In difficult times with turbulent environments, unstable staff, and disconnections between healthcare settings (e.g., hospital and community settings), it would be difficult to link the occurrence of UNC to the different potential outcomes at the patient, nurse, and organisational levels [ 5 , 12 , 13 , 14 ]. However, the occurrence of UNC may have bolstered the negative effects of other widely observed phenomena, such as the decreased accessibility and continuity of care observed during the pandemic, thus indirectly affecting the health outcomes at both the individual and collective levels (e.g., reduced screening, reduced care for cancer patients) [ 80 , 81 ].

Limitations

This review has several limitations. First, databases were searched using well-known established keywords in the field, strictly connected with the conceptual definitions in the field and with the tools measuring the phenomenon. Moreover, given that no MeSH terms have been established in the field, researchers used keywords. Consequently, some studies may have been missed. Second, studies whose data collection period was uncertain or ambiguous (e.g., started before or during the pandemic) were excluded. Moreover, studies not using validated instruments with available reliability and validity data were also excluded, and these decisions may have introduced a selection bias. Furthermore, grey literature was not assessed, introducing additional selection bias. Third, we included only articles written in English, Turkish, or Italian, so the comprehensiveness of this review could have been threatened by the exclusion of other languages. Fourth, in the data analysis and synthesis process, an approach was adopted aiming at ensuring accuracy given the different measurement tools used in the field. Moreover, the data analysis process was conducted in an innovative manner by considering each intervention or reason at the granular level (the order, according to the statistical values) instead of the global level (global scores). This may have provided clarity, but it may have compromised the depiction of a global picture of the phenomenon. No previous similar approaches have been used in this field. Accumulating evidence with additional studies, such as summarising findings in the post-pandemic era, may corroborate the analytical strategy used.

UNC studies produced during the pandemic documented the occurrence of the phenomenon and its reasons mainly in the first and second waves of the COVID-19 pandemic. These studies were conducted mainly in Europe and Asia, which were the first to be dramatically affected by the pandemic. The studies involved multicentre units in the attempt to measure the whole response of the healthcare settings, mainly using the MISSCARE Survey with descriptive intents and using quality, sound research methodologies.

At the overall level, those nursing care activities that were mostly unfinished during the pandemic are substantially the same as those reported in the pre-pandemic era, suggesting that nurses applied the same prioritisation responses in difficult times. However, interesting intra- and inter-country differences emerged: those countries new to measuring unfinished care reported different patterns compared to those seen in Europe and the US, where this research is well established; they also reported intra-country variations, suggesting an interesting new course of research in the field. The new patterns that emerged should be better investigated through post-pandemic studies to discover whether they reflected the decision-making process during difficult conditions or a different prioritisation process.

Across studies, the primary reasons for UNC were listed as labour resources, followed by other specific reasons related to organisational, work, and individual variables. Substantially, the evidence is in line with that previously documented. However, findings are consistent across studies, suggesting that health services experienced similar pressure worldwide. Moreover, several emotional factors have been investigated among nurses, revealing their important role in triggering UNC. This level should be investigated further, considering the long-term consequences of the pandemic on the well-being of the workforce. Given that no studies have attempted to measure the UNC consequences, more efforts are also required in this direction.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

Basel Extend of Rationing of Nursing Care

Basel Extent of Rationing of Nursing Care for Nursing Homes

Basel Extent of Rationing of Nursing Care Revised

Cumulative Index to Nursing and Allied Health Literature

Coronavirus-19

Intensive Care Unit Omitted Nursing Care instrument

Perceived Implicit Rationing of Nursing Care

Preferred Reporting Items for Systematic Reviews and Meta Analysis

Unfinished Nursing Care

Unfinished Nursing Care Survey

World Health Organization

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Bayram, A., Chiappinotto, S. & Palese, A. Unfinished nursing care in healthcare settings during the COVID-19 pandemic: a systematic review. BMC Health Serv Res 24 , 352 (2024). https://doi.org/10.1186/s12913-024-10708-7

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Artificial intelligence and illusions of understanding in scientific research

  • Lisa Messeri   ORCID: orcid.org/0000-0002-0964-123X 1   na1 &
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Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

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We thank D. S. Bassett, W. J. Brady, S. Helmreich, S. Kapoor, T. Lombrozo, A. Narayanan, M. Salganik and A. J. te Velthuis for comments. We also thank C. Buckner and P. Winter for their feedback and suggestions.

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research article using nursing theory

This paper is in the following e-collection/theme issue:

Published on 19.3.2024 in Vol 26 (2024)

Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022

Authors of this article:

Author Orcid Image

Original Paper

  • Daemin Park 1 , PhD   ; 
  • Dasom Kim 2 , PhD   ; 
  • Ah-hyun Park 3 , PhD  

1 School of Media & Communication, Sunmoon University, Chungcheongnam-do, Republic of Korea

2 Home Visit Healthcare Team, Expert Group on Health Promotion for Seoul Metropolitan Government, Seoul, Republic of Korea

3 Tobacco Control Team, Expert Group on Health Promotion for Seoul Metropolitan Government, Seoul, Republic of Korea

Corresponding Author:

Dasom Kim, PhD

Home Visit Healthcare Team

Expert Group on Health Promotion for Seoul Metropolitan Government

#410, Life Science Building.Annex, 120, Neungdong-ro, Gwangjin-gu

Seoul, 05029

Republic of Korea

Phone: 82 1072040418

Email: [email protected]

Background: In recent years, Korean society has increasingly recognized the importance of nurses in the context of population aging and infectious disease control. However, nurses still face difficulties with regard to policy activities that are aimed at improving the nursing workforce structure and working environment. Media coverage plays an important role in public awareness of a particular issue and can be an important strategy in policy activities.

Objective: This study analyzed data from 18 years of news coverage on nursing-related issues. The focus of this study was to examine the drivers of the social, local, economic, and political agendas that were emphasized in the media by the analysis of main sources and their quotes. This analysis revealed which nursing media agendas were emphasized (eg, social aspects), neglected (eg, policy aspects), and negotiated.

Methods: Descriptive analysis, natural language processing, and semantic network analysis were applied to analyze data collected from 2005 to 2022. BigKinds were used for the collection of data, automatic multi-categorization of news, named entity recognition of news sources, and extraction and topic modeling of quotes. The main news sources were identified by conducting a 1-mode network analysis with SNAnalyzer. The main agendas of nursing-related news coverage were examined through the qualitative analysis of major sources’ quotes by section. The common and individual interests of the top-ranked sources were analyzed through a 2-mode network analysis using UCINET.

Results: In total, 128,339 articles from 54 media outlets on nursing-related issues were analyzed. Descriptive analysis showed that nursing-related news was mainly covered in social (99,868/128,339, 77.82%) and local (48,056/128,339, 48.56%) sections, whereas it was rarely covered in economic (9439/128,339, 7.35%) and political (7301/128,339, 5.69%) sections. Furthermore, 445 sources that had made the top 20 list at least once by year and section were analyzed. Other than “nurse,” the main sources for each section were “labor union,” “local resident,” “government,” and “Moon Jae-in.” “Nursing Bill” emerged as a common interest among nurses and doctors, although the topic did not garner considerable attention from the Ministry of Health and Welfare. Analyzing quotes showed that nurses were portrayed as heroes, laborers, survivors of abuse, and perpetrators. The economic section focused on employment of youth and women in nursing. In the political section, conflicts between nurses and doctors, which may have caused policy confusion, were highlighted. Policy formulation processes were not adequately reported. Media coverage of the enactment of nursing laws tended to relate to confrontations between political parties.

Conclusions: The media plays a crucial role in highlighting various aspects of nursing practice. However, policy formulation processes to solve nursing issues were not adequately reported in South Korea. This study suggests that nurses should secure policy compliance by persuading the public to understand their professional perspectives.

Introduction

Controversy over nursing legislation in south korea.

The COVID-19 pandemic has renewed the focus on the importance of nurses in South Korea’s health care system with a resultant increase in the interest in improving the nursing workforce structure, working conditions, and treatment [ 1 ]. Although the pandemic has fueled a movement to improve nursing policies in South Korea, nurses face difficulties in nursing-related policy activities because of various stakeholders and policy environments. As the nursing profession is regulated by the Medical Service Act enacted in 1951, rather than an independent law such as the Nursing Bill, there are concerns about the inadequacy of the legal status or rights of nurses and, consequently, their diminished role in the health care system.

In South Korea, the debate over nursing legislation has been ongoing for more than a decade. In 2005, Kim Sun-mi, a representative of the ruling Uri Party, first proposed nursing legislation—the Nursing Bill—in the Korean National Assembly; however, the bill was abandoned. In 2018, the bill was reintroduced and rejected and was eventually passed in the National Assembly in April 2023 as the Nursing Bill, shortly after the COVID-19 pandemic ended. However, President Yoon Suk Yeol vetoed the Nursing Bill, and the bill was rejected again in May 2023.

Compared with the early days of the COVID-19 pandemic, it is presently difficult to obtain a favorable public opinion among the public and other professions at the policy level. This may be because of the differences between the general public’s interests in nurses’ workforce structure, work, and treatment and the need for nursing-related policies [ 2 , 3 ].

Research Context: The Importance of Media in Health Policy

Media coverage has an important impact on shaping public opinion on certain issues. The policy agenda–setting theory suggests that media coverage of an issue can influence the public agenda, which in turn can influence the policy agenda [ 4 , 5 ]. When the media continues to report on an issue, it draws public attention, which can lead to an increased public demand for action from policymakers. Thus, media coverage can be viewed as a form of valuable large-scale data that reflects the flow of social interest and changes in a particular phenomenon or issue. These data can be used to identify paradigm shifts in policies related to the issue.

With the aging population and increasing prevalence of chronic diseases, the medical burden would increase in South Korea. Furthermore, changes in the social environment and policies, such as the introduction of integrated nursing care services, affect the demand for nursing care. In this context, it is important for the public and policymakers to understand nursing care accurately. Media coverage has a significant impact on public perception of medical issues [ 6 , 7 ]. However, research on media coverage in nursing is still limited.

Previous studies focused on identifying the images of nurses in the media or analyzing individual issues [ 1 , 8 - 11 ]. Both the original Woodhull study conducted in 1997 and a subsequent study conducted in 2017 examined the representation of nurses in health-related news coverage. However, both studies had limitations in that the research involved analysis of news from a single year and focused solely on the health-related context without exploring nurses’ representation as news sources in all news sections [ 12 , 13 ].

Methodologically, several studies have used natural language processing (NLP) of news data, topic modeling, and network analysis [ 8 - 11 , 14 ]. However, most studies analyzed articles over a short period of less than a year [ 8 - 11 , 14 ]. This strategy has limited the scope of these studies and prevented them from capturing the entire range of issues faced by the nursing community. Additionally, the short timeframe of these studies makes it difficult to capture the long-term development and context of policymaking.

For collecting and NLP of data, we used BigKinds, the largest public news database in South Korea, comprising over 80 million news articles published starting in 1990 [ 15 , 16 ]. It was developed by the Korea Press Foundation, a governmental organization, to support the copyright business of news media. BigKinds is widely used for academic research of news analysis in various fields including nursing studies [ 9 , 11 ]. This study also conducted network analysis and qualitative analysis of quotes.

This study examined how nursing-related issues have changed over time by analyzing, by year and section, approximately 130,000 articles that were published in 54 major Korean media outlets over an 18-year period.

This study focuses on the sources of news articles and their quotes. Sources are key informants whom a reporter has heard directly from and therefore cited in the news for a direct quote [ 17 ]. In communication studies, sources are the most important elements of news writing. News is a description of what a source has said [ 18 ]. Thus, citing sources is a core factual practice in journalism, along with presenting figures and being present at the scene for direct observation [ 19 ].

This study aimed to examine the drivers of the social, local, economic, and political agendas emphasized in the media by the analysis of main sources and their quotes. This analysis revealed which nursing media agendas were emphasized (eg, social aspects), neglected (eg, policy aspects), and negotiated. Furthermore, this study determined the way in which policy formulation processes were not adequately reported.

Research Questions

The research questions (RQs) of this study are as follows:

  • RQ1: How many articles were reported by year and section?
  • RQ2: Who have been the main sources of media coverage of nurses since 2005, by year and section?
  • RQ3: What concerns did the health care community, in particular, nurses, medical doctors, and the Ministry of Health and Welfare (MOHW), pay more attention to and what were their common concerns?
  • RQ4: What are the key issues of nursing controversies in the press in quotes?

RQ1 was researched by descriptive analysis; RQ2 was researched by analyzing the news source network; was researched RQ3 by analyzing the news source-topic network; and RQ4 was researched by an analysis of quotes from key sources. This strategy will allow us to see who has led the agenda-setting of nurse-related issues and to identify the agenda that has been emphasized by each section in the media.

Study Design

Descriptive analysis, NLP, and semantic network analysis were applied to analyze data collected from 2005 to 2022 using BigKinds (Korea Press Foundation), SNAnalyzer, and UCINET. First, BigKinds were used for the collection of data, descriptive analysis, automatic multi-categorization of news, named entity recognition (NER) of news sources, and extraction of quotes from news articles. Second, the main news sources were identified by conducting a 1-mode network analysis with SNAnalyzer. Third, the main agendas of nursing-related news coverage were examined by conducting the qualitative analysis of major sources’ quotes by section. Fourth, the common and individual interests of the top-ranked sources were analyzed through a 2-mode network analysis using UCINET.

Data Collection

The collection of news data with NLP was conducted using BigKinds. We used 1 search term “간호사,” which means “nurse” in Korean. It is rare that an article about nurses does not mention “nurses”; rather, there may be an article that contains the word “nurse” but indirectly deals with nurses. Articles wherein nurses were peripherally featured were not excluded as they could provide context for nurse coverage.

This study collected data from sources and quotes in nurse articles published in South Korea from January 1, 2005, to December 31, 2022. In 2005, an independent nursing law was proposed for the first time in South Korea by a member of the National Assembly.

The news articles were collected from 54 major domestic media outlets, including national, economic, local, broadcasting, and specialized newspapers, and provided by BigKinds.

Natural Language Processing

BigKinds provides various advanced NLP functions, including automatic article classification, NER, quote extraction, mapping of sources and quotes through semantic analysis, and auto-tagging and ranking of topics in each quote through topic modeling.

The news analyzed was selected from 4 sections—social, local, economic, and political—which are highly related to the policy aspects of nurses and contain numerous articles. The sections are automatically classified by BigKinds. Automatic classification overcomes the differences in naming sections between years and media outlets. Each article can be categorized into a maximum of 3 different sections. For instance, a political article dealing with regional issues can be classified into both political and local sections simultaneously.

Source extraction is accomplished through NER for names, affiliations, and job titles. According to the manual, BigKinds’ NER uses an algorithm that combines structured support vector machine and Bidirectional Encoder Representations from Transformers (BERT) with an F 1 -score of 91.5%.

We extracted quotes from BigKinds and mapped sources to their quotes through semantic analysis. This study focused on major sources’ quotes.

Data cleansing was done in 2 stages. In the first stage, we removed incorrect sources based on character count, part of speech, and so on. Furthermore, we removed anonymous sources and remaining institutional sources. We kept sources who were labeled as a profession or group, such as “nurses” or “hospitals,” and who were closely related to the research topics.

The second round of cleansing was conducted after ranking the sources according to the news source network analysis described later. Given the nature of semantic networks, incorrect results in NLP tend to be marginalized by low rankings. Therefore, rather than refining the entire data set from the beginning, it is more efficient to refine only the top-ranked sources who are candidates for the analysis.

In this study, we first performed a second round of cleansing on the top 100 sources. If there was a synonym for the name of an organization due to using abbreviations or anaphoras, we kept the higher-ranked source and unified them with the same name. Through this process, the top 20 news sources by section and year were selected. All the different sources that were tied around the 20th position of the cutoff were included. Finally, 445 sources that had made the top 20 list at least once by year and section were selected. We present the top 20 sources in each section based on cumulative yearly degree centrality, summarized briefly.

Descriptive Analysis

BigKinds provides the number of articles related to searching keywords by year and section. The total number of articles is deduplicated but the sum of the number of articles by section can exceed the total number of articles because each article is categorized into up to 3 sections.

Semantic Network Analysis

In this study, thousands of sources were cited in the articles related to nursing for decades. Thus, semantic network analysis was performed to rank sources by year and section and determine the relationship between sources and topics. Specifically, news source network analysis and news source-topic network analysis were conducted.

The news source network is an undirected 1-mode network with sources as nodes and article cooccurrences as edges. The importance of each source is evaluated based on the degree centrality of the news source network. A source with a high degree centrality appears in many articles and is discussed in many agendas with many different sources [ 16 ]. We also provide descriptive information (size, number of edges, and density) of networks.

A program called SNAnalyzer was used for network analysis [ 16 , 20 ]. SNAnalyzer analyzes multiple Excel files simultaneously with folder-to-folder input and output and provides data cleansing, file name standardization, degree centrality, tie strength, ranking, and descriptive statistics for up to 1,048,575 nodes per file.

NetDraw of UCINET software was used to visualize the news source-topic network. News source-topic network analysis is a 2-mode undirected network with sources and quote topics as nodes and quote cooccurrences as edges. The strength of networks shows the number of times the sources mentioned each topic. News source–topic network analysis can be used to determine which topics are of common interest to sources, and which are of interest only to specific sources. This study focused on 3 groups of sources: nurses, doctors, and the MOHW. Each group was merged according to their affiliations to compare interests at the organizational level.

Quote Analysis

This study also performed quote analysis as a qualitative analysis to deeply understand the context of nursing in news media. In quote analysis, we focused on quotes uttered by the top 445 sources of each year and each section and contained the word “nursing” as a topic in the quotes. Because most top sources were highly cited, analyzing the quotes alone from the top sources resulted in analyzing a substantial number of citations. The number of quotes containing “간호 (nursing)” was 26,926. The total number of deduplicated quotes was 162,316.

Ethical Considerations

Due to the nature of the research involving nonhuman participants, this study was exempt from review by the institutional review board of SM University (202302-001-1). Researchers collected publicly available existing data.

The total number of articles was 128,339, excluding duplicate articles. The number of articles per section was 99,868 for the social section, 48,056 for the local section, 9439 for the economy section, and 7301 for the political section. A given article was classified as a duplicate in up to 3 sections.

Figure 1 shows the frequency distribution of the articles by year and section. The years 2009, 2015, and 2020 saw surges in news articles corresponding to the swine flu, Middle East respiratory syndrome (MERS), and COVID-19 epidemics, respectively. In 2018, a “burning” incident—a metaphor for bullying in the workplace—occurred among nurses at the Asan Medical Center in Seoul. This incident led to a rapid increase in the number of articles that focused on the nursing culture. These results suggest that the media paid considerable attention to the response to new infectious diseases and accidents in the nursing profession.

research article using nursing theory

Semantic Network Analysis: News Source Network Analysis

There were 72 news source networks. We present the size, number of edges, and density of these networks as network-level descriptive statistics in Multimedia Appendix 1 .

Multimedia Appendices 2 - 5 present the top 20 sources by centrality value, which represents the level of connectivity in terms of the years and sections. Each source’s rank is determined as the sum of the links. Sources related to the social aspect include incidents and accidents, medical and health issues, education, labor and welfare, trade unions, and activities in civil society. Local governments, such as the Seoul metropolitan government, police, and courts, serve as major sources for the social departments of media companies. Therefore, nurses and police are recognized as important sources in the social sector.

The main sources representing each section are briefly presented in the following sections with tables in the Multimedia Appendices. The interests of main sources are discussed in more detail in the sections on news source-topic network analysis and quote analysis.

Social Section

“Nurses (the total sum of the degree centrality is 2091)” were the most important sources in the social section of the media, especially in crucial cases such as the 2009 H1N1 influenza epidemic (degree centrality in 2009 is 65), the 2013 Jinju Medical Center incident (sum of degree=74), the 2015 MERS incident (sum of degree=117), and the 2016 nurse workplace bullying and medical malpractice controversy (sum of degree=130). The above-described importance persisted through 2018 (sum of degree=176) and was further emphasized by the COVID-19 pandemic, which began in 2020 (sum of degree=351) in a different context. During the COVID-19 pandemic, governmental sources, such as Central Disaster Management (242 in 2021), came to the forefront.

The period when the “hospital” source (the total sum of the degree centrality is 1790) was considered important coincided with the period when the “nurse” source served as an important source. This was mainly because the hospital was both a nurse’s workplace and a medical malpractice site. The importance of the “police” source significantly increased in 2018 (sum of degree=158) when the issue of nurse suicide by bullying and medical malpractice gained social prominence.

Local Section

“Hospital” (577 in total) is the most important source in the local section, especially in 2009 (sum of degree=42), 2014-2015 (sum of degree=34 and 38), and 2017-2021 (sum of degree=54, 36, 39, 103, and 70). During these periods, various impertinent issues were highlighted, including overwork, poor working conditions, and subsequent turnover within the hospital setting. Trade unions have emerged as crucial sources and have thereby contributed to the discourse surrounding the identified issues.

Related to the “labor union” (207 in total), specific incidents that garnered significant attention included the decision to close the Jinju Medical Center in South Gyeongsang Province in 2013 (sum of degree=37), which was criticized for the perceived moral hazard potential. Additionally, the general strike organized by the health care union in 2021 (sum of degree=36) in regard to the expansion of doctor numbers and the adjustment of the patient-to-nurse ratio became a topic of extensive discussion.

The media have played a vital role in disseminating information and shaping public opinion during the COVID-19 pandemic. Of particular interest were the decisions made by the local governments and their respective heads with regard to quarantine measures. For example, during the COVID-19 outbreak in Daegu in early 2020, Daegu’s health care and quarantine systems were quickly overwhelmed, and media coverage of the crisis was extensive. “Kwon Young-jin” (135 in 2020), the mayor of Daegu Metropolitan City, appeared in the news, as well as “Gwangju Metropolitan Government” (26 in 2020), which helped Daegu during its time of need. In July 2020, Daegu extended its helping hand to Gwangju when the latter faced a shortage of hospital beds due to the surge in COVID-19 cases. This act of solidarity and collaboration exemplifies the importance of interregional cooperation during public health emergencies.

Furthermore, the enactment of local government ordinances aimed at supporting essential workers, including nurses, in a non–face-to-face environment, as necessitated by the COVID-19 pandemic, is noteworthy. In this context, “Chong Won-o” (76 in total), head of the Seongdong-gu District Office in Seoul, enacted ordinances on safety measures, web-based work-support systems, and psychological counseling for essential workers such as nurses.

Economic and Political Section

The main sources of the economic news were related to free trade agreements (FTA). Unions for youth and women employment and corporate sources were also prominently featured. President “Park Geun-hye” (24 in total), who emphasized economic development by Korean nurses dispatched to Germany in the 1960s and 1970s, played a pivotal role as the primary source of economic development. During the COVID-19 pandemic, local governments became important in the economic sector.

Regarding the political section, the main sources have traditionally been the president and politicians affiliated with major political parties (ie, Ha Tae-keung, 79 in total). In the context of the COVID-19 pandemic, these sources emerged as a major source, as President “Moon Jae-in” (577 in total) engaged in a verbal dispute over a post on Facebook that praised the diligent efforts of nurses.

Semantic Network Analysis: News Source-Topic Network Analysis

A visualization of the news source-topic network for the top 50 topic words by health care source is shown in Figure 2 . The analysis revealed 15 common topic words that were prominent among nurses, doctors, and the MOHW. The top common terms encompassed general health-related topics such as “nurses,” “patients,” “medical staff,” and “hospitals.” Notably, pandemic-related topics like “COVID-19,” “MERS” “confirmed cases,” and “infectious diseases” also featured prominently as shared interests.

research article using nursing theory

“Nursing Bill” emerged as an issue of high interest among sources from the nursing and doctor groups although the topic did not garner significant attention from MOHW sources. Interestingly, both doctors and MOHW sources exhibited common interests in “the Medical Act” and “amendments,” although nurses mentioned the Medical Act less frequently. Moreover, the nurse group and MOHW sources shared a major concern with regard to the “community,” indicating a consensus on the importance of expanding public health care in the community.

Nurses demonstrated a distinct interest in education-related themes, such as “students” and “nursing department,” as well as concerns regarding poor working conditions, including “protective clothing” and “working environment.” Additionally, professional pride was evident through terms like “sense of mission” and “nightingale.” Unfortunately, workplace harassment, represented by the term “burning,” has emerged as an important issue for nurses.

Conversely, physician sources displayed a particular interest in “operating room” and “closed-circuit television (CCTV).” Doctors voiced opposition to the implementation of closed-circuit television surveillance in operating rooms to prevent medical errors. Furthermore, they expressed concerns about “telemedicine” and “Oriental medicine,” fearing potential infringement of doctors’ status. Their apprehension lies in the possibility that nondoctor medical practitioners, such as nurses and herbalists, may perform medical treatments and thereby exclude doctors through telemedicine platforms.

Social Sections

The timeline and main topics of interest in articles related to nurses are detailed in Multimedia Appendix 6 . Nurses have been portrayed in many ways in the social sector, and these portrayals have encompassed various roles that range from heroes to workers to survivors of abuse and even to perpetrators.

Nurses have been portrayed as heroes and dedicated nightingales who have been fighting at the frontlines of epidemic prevention to stop the spread of various infectious diseases—from swine flu and MERS to COVID-19. Such portrayals frequently emphasize courage, compassion, and dedication [ 1 ]. Besides their role in epidemic prevention, nurses’ heroic images extend beyond this domain and encompass their initial responses to various mass casualty incidents. An illustrative example is the Itaewon tragedy of 2022, wherein the remarkable efforts of nurses were at the forefront. One nurse said, “I think I performed CPR so extensively that I lost count of the number of individuals I attended to” [ 21 ].

Beyond this heroic image, other reports have emerged which indicate that nurses face unfavorable working conditions like those of laborers. These conditions are characterized by excessive workloads, inadequate compensation, and lack of recognition of their invaluable contributions. Nurses’ voices in the social media coverage extend beyond merely conveying the difficulties associated with nursing work. Furthermore, nurses serve as valuable sources who articulate calls for policy alternatives or provide evaluations of existing policies. This multifaceted portrayal of nurses, which highlights their roles as workers, experts, and heroes, is observed both in the national and international media [ 22 , 23 ].

Furthermore, nurses appeared in the media as suspects or perpetrators of medical accidents, such as the death of a newborn at Ewha Womans University Mokdong Hospital in 2017, and as key sources in cases involving surrogate doctors. However, nurses are not responsible for medical errors and are described as enforcers who function under the supervision of hospitals and doctors.

Nurses have progressed beyond communicating difficulties in the nursing workplace and whistleblowing incidents to initiating calls for policy alternatives and evaluating ongoing policies in media reports. In particular, the Korean Nursing Association emerged as a major source, besides nurses, when discussions of the Nursing Care Act began to be earnestly covered in the media. Simultaneously, the Korean Medical Association strongly opposed the nursing law.

Local Sections

In the local section, there was further emphasis on poor health care and working conditions, which were revealed in the social section. Primary sources associated with “hospitals” have voiced concern with regard to the challenges that are encountered within local health care facilities, particularly in socioeconomically disadvantaged regions. Throughout different infectious disease outbreaks, including the pandemic, MERS, and COVID-19, the focal points of the discussion included the burdensome workloads imposed on nurses, inadequate staffing levels, and insufficient resources, including facilities and equipment.

This situation underscores the need for an expanded role of nurses within the community, greater recognition of the nursing profession, and enhanced public awareness of health care issues. Public and governmental sources, such as the MOHW, nursing organizations, and unions, have advocated the need for increased investment in public health care. The provision of health care services based solely on market principles is limited to regions characterized by smaller populations and relatively lower incomes. Consequently, the government has endeavored to fortify local public health care by emphasizing national university hospitals and public health centers as central hubs.

Various measures have been pursued to achieve the above-described goal. These measures include legislative initiatives for the establishment of community and professional nurse-systems, as well as the expansion and reinforcement of the nursing profession’s role through mechanisms such as the nursing grade system and the expansion of nursing schools. Moreover, public support has been provided to bolster these efforts. Although it is acknowledged that challenges, including trial and error, disagreements within the nursing profession, and conflicts with other health care organizations, such as the Korean Medical Association, have emerged during this process, the media generally presents the strengthening of local public health care as being a step toward aligning with the enhanced status of the nursing profession.

In the case of the closure of Jinju Public Medical Center in 2013 and the health care union’s call for a general strike in 2021, “unions” appear as the main source of information, calling for the strengthening of public health care. The nursing profession, along with various sources from government departments, such as the MOHW, emphasizes the need to bolster public health care in local media discourse. Media reports portray the government as actively seeking to fortify local public health care with a focus on local university hospitals and public health centers. Specifically, various policies were mentioned, such as the introduction of nurse practitioners, visiting nurses, dispatch nurses, nurses in training, part-time nurses, comprehensive nursing services, a nursing grading system, more nursing schools, and public health nurses. These policies aim to improve the local health care environment by expanding and strengthening the roles of nurses and providing public support.

Economy Sections

Between 2005 and 2007, the opening of the health care market emerged as a significant economic agenda within the context of the Korea-US FTA. Consequently, notable figures such as Kim Jong-hoon, who was the Korea’s chief representative in the US-Korea FTA; Kim Hyun-Jong, who served as the head of the Trade Negotiations Division at the Ministry of Foreign Affairs; and Wendy Cutler, who was the US chief representative, became a key source in the economy section of the media.

Unions represent another significant source in the economic sector, particularly in relation to the interests of nurses. Various unions, including major hospital unions, the National Healthcare Workers’ Union, the Democratic Trade Union Confederation (to which the National Healthcare Workers’ Union is affiliated), and the Public Transport Workers’ Union Medical Solidarity Headquarters (affiliated to the Public Transport Workers’ Union) have been actively engaged in shaping the discourse on nurses’ issues within the broader framework of labor concerns. These unions shed light on key topics, such as insufficient nurse recruitment, the growing trend of casualization through the introduction of part-time work, and substandard working conditions for women employees. The sources from the “union” sector on the economy section of the media provide valuable insights that reflect a broader sense of solidarity with the collective of nurses.

In addition to the heroic portrayal of nurses during the pandemic, the use of nurse imagery in economic development is observed, as exemplified by the depiction of the “Korean nurses in West Germany” as an economic development hero. President Park Geun-hye has associated the concepts of “Korean nurses to West Germany,” along with “Korean miners to West Germany” and “Middle Eastern construction workers,” with overseas employment initiatives that are aimed at combatting youth unemployment and interlinking nurses within the economic policy direction.

Political Sections

Politicians from major parties and heads of government ministries, including Moon Jae-in, have emerged as key sources. During the COVID-19 pandemic in 2020 and 2021, President Moon found himself amid a controversy. The president is expected to express gratitude to the health care workers, particularly nurses, doctors, and pharmacists.

His emphasis on the nurses’ hard work became a point of political contention. A notable example was the 2020 controversy surrounding the division of medical staff at a time when doctors were on strike to protest the government’s plans to increase the number of seats in public medical schools and establish new public medical schools. On September 2, 2020, President Moon wrote a Facebook post stating, “Nurses are silently guarding medical sites where doctors, including specialists, have left,” and highlighting that the majority of medical staff referred to as “doctors” were actually nurses. These statements drew criticism from members of the opposite People’s Power Party, such as the representative Ha Tae-kyung and spokesperson Kim Eun-hye, who accused President Moon of dividing the medical staff based on their profession. In contrast, the ruling Democratic Party of Korea and representative Ko Goo-jung refuted the division of the medical staff frame and countered that it was the People’s Power Party that created a division between the government and medical staff. Furthermore, debates surrounding the authorship of Facebook posts added another layer of controversy to the situation.

In the political section, the news articles featured a range of stakeholders, including the nursing community, who voiced their perspectives on the Nursing Bill. However, rather than providing an in-depth analysis of the policy’s specific content, media coverage has primarily emphasized the conflict between nurses and doctors with regard to the enactment of the bill, as well as the debates between President Moon Jae-in and the opposition parties.

The increased media attention on the nursing law enactment began in earnest under President Moon Jae-in’s administration after the COVID-19 pandemic, and the controversy surrounding the enactment of the nursing law that represented the ongoing conflict between the Korean Nurses Association and the Korean Medical Association has also received significant attention. The conflict over the Nursing Bill was manifested in various ways, such as the resolution of a general strike by nurses in 2021 and the boycott of the national nursing examination in 2022. The strategies used by nurses in their struggle, which encompassed labor disputes with unions and profession-wide refusal to participate in the national examination, reflect the dual nature of their roles as both workers and professionals.

Following the public’s widespread recognition and support for the dedicated efforts of nurses during the COVID-19 pandemic, the Nursing Bill has resurfaced as an important issue that has led both the major presidential candidates in the 2022 election to pledge their commitment toward enacting nursing legislation. However, a divergence in approach has become apparent, with the newly elected People’s Power Party expressing caution following internal disagreements. Contrarily, the Democratic Party affirmed its intent to pursue the enactment of the nursing legislation. This eventually resulted in the president vetoing the legislation that had been passed by the opposition-led National Assembly in 2023.

Principal Findings

This study focused on analyzing media coverage of the nursing agenda in South Korea over an 18-year period starting in 2005 and examined the coverage patterns, sources responsible for reporting, and quotes in articles, both annually and across different sections. To achieve this objective, a large data set comprising nurse-related articles from BigKinds was used.

In sum, 128,339 articles from 54 media outlets on nursing-related issues were analyzed. The news was mainly covered in social and local sections. According to news source network analysis, 445 main sources who had made the top 20 list at least once by year and section were selected. Multimedia Appendices 2 - 5 show the top 20 sources by year and section.

The news source-topic network analysis highlights nursing legislation as a common concern for intense conflict between doctors and nurses. The “Nursing Bill” emerged as a common interest among nurses and doctors although the topic did not garner considerable attention from the MOHW.

This study aimed to examine the drivers of the social, local, economic, and political agendas emphasized in the media by the analysis of main sources and their quotes. In the social section, various issues were covered, including the COVID-19 response, workplace bullying, and nursing bills. On the local level, poor working conditions and strengthening public health care were key issues. Considering the economic aspect, labor issues and overseas work were hot topics. On the political aspect, conflicts between nurses and doctors over the nursing bills and the resulting controversy were crucial. This study revealed the media’s evolving portrayal of nurses over time. Nurses are portrayed in various roles, such as heroes, workers, survivors of abuse, and even perpetrators, across different topics covered by the media. Nonetheless, in the economic and political spheres, nurses’ voices tend to diminish. Media coverage of the enactment of nursing laws tends to move on confrontations between political parties.

Comparison to Prior Work

Aber and Hawkins [ 24 ] undertook a comprehensive examination of advertisements featured in medical and nursing journals. Their findings elucidated a prevailing depiction of nurses in print media, where they were predominantly portrayed as ornamental figures, sexualized objects, or ancillaries to doctors. Hoyle et al [ 25 ] embarked on a comprehensive exploration of nurses’ perceptions regarding the media’s influence on the public understanding of the nursing profession. They discerned recurrent themes, notably the media’s negative portrayal of the nursing profession—a sentiment echoed in other studies [ 26 ]. In contrast, the severe acute respiratory syndrome crisis served as a crucial juncture in the media representation of nurses, akin to the role of the COVID-19 pandemic in South Korea [ 9 , 10 , 27 ]. These preceding studies have demonstrated the media’s paramount influence in shaping societal perceptions of nursing.

Our study underscores a more dynamic evolution of media portrayals pertaining to nurses. During health crises, the media consistently lionized nurses, portraying them as the unsung heroes on the frontlines. However, media portrayals of nurses have undergone fundamental transformations, mirroring the societal evolutions and the multifaceted challenges encountered by the nursing fraternity. With South Korea confronting the challenges of an aging demographic, there is an evident amplification in nurses’ community-based responsibilities. This evolution signifies a transition from a traditionally passive representation to a more proactive and central role transcending their conventional subordinate position relative to doctors.

Media coverage can influence not only the image of nurses but also their working conditions, the structure of the health care workforce, and the legislative process [ 8 , 26 , 28 ]. The paradigm shift in the media agenda has precipitated heightened political and juridical tensions between the medical and nursing professions. Conflicts between health care professionals, as evidenced by the political dimension analysis, can impede policymaking and result in policy confusion due to politicization. Previous instances of policy confusion within the health care community, including health insurance integration, medical division of labor, insurance finance policy, and reimbursement policy, have been attributed to conflicting interests and backlash from targeted groups [ 29 , 30 ]. Moreover, these factors have been reflected in the media representations.

Limitations

This study provides a comprehensive analysis of media coverage of nurses over an 18-year period since 2005, with a focus on the sources. However, this study has several limitations.

First, the study analyzed only major newspapers and broadcast media that are listed on BigKinds, which means that data from health care journals that were not included in BigKinds could not be analyzed. Future research could compare agenda-related differences between the mass media and health care professional journals to explore how variations in general and public agendas influence policy agendas. Additionally, it would be valuable to examine discussions on platforms, such as Twitter and blogs, to capture a broader range of perspectives.

Second, as this study’s analysis was based on all articles containing the keyword “nurses,” this strategy limited the ability to descriptively delve into specific agendas. A follow-up study could focus on analyzing a specific agenda, such as the Nursing Bill, over an extended timeframe to gain a more nuanced understanding of policymaking through media channels.

Third, this study implies that health care policy is not solely based on scientific advancements or socioeconomic justifications but is inherently political. Strategic media outreach and policy research are necessary to elucidate policy directions that nursing communities aim to pursue.

Conclusions

This study used a combination of NLP and semantic network analyses to examine how nurse-related issues were covered by the media, specifically focusing on sources and quotes. The media plays a crucial role in highlighting various aspects of nursing practice and nurses, thereby contributing to political engagement and policy activism. However, policy formulation processes to solve nursing issues have not been adequately reported in South Korea.

When engaging with the media to promote nursing policy, it is crucial to navigate the politicization of issues and focus on how policy matters are prioritized on the agenda. As advocates for public health, nurses are responsible for actively engaging in legislative and policymaking processes from a political standpoint [ 31 - 33 ]. However, nurses may not fully realize the extent to which nursing practice relies on public policy decisions and their potential to shape those decisions [ 34 , 35 ]. Nurses should participate in various activities at different levels, such as attending public forums and giving media interviews, to voice their professional views and positions as advocates [ 36 , 37 ]. Nurses need political competence to address the broader determinants of health, effectively intervene in culturally diverse societies, collaborate in developing humane health care systems, and bring nursing values to policy discussions [ 38 ].

Acknowledgments

This study was supported by the SunMoon University Research Grant in 2021 (Number 2022-007). The authors used ChatGPT and DeepL for the initial English translation.

Data Availability

Data are not available to share due to Department of Veterans Affairs and ethical retractions; however, annotation instructions and vocabulary are available upon request..

Authors' Contributions

Conceptualization and methodology were developed by DP. Data curation and analysis tasks were performed by DP. DP secured funding for the project. The investigation involved DP, AHP, and DK. Project administration and supervision were handled by DP, AHP, and DK. Resources and software management were under the purview of DP. Validation was conducted by DP, AHP, and DK. Visualization tasks were executed by DP. Writing of the original draft and review and editing were collaborative efforts involving DP, AHP, and DK.

Conflicts of Interest

None declared.

The size, number of edges, and density of 72 news source networks.

Top 20 news sources by year in the social section.

Top 20 news sources by year in the local section.

Top 20 news sources by year in the economy section.

Top 20 news sources by year in the political section.

The timeline and main agenda in nurse articles.

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Abbreviations

Edited by T Leung; submitted 03.07.23; peer-reviewed by J Delgado-Ron, C Zhao, S Pesälä; comments to author 19.10.23; revised version received 09.11.23; accepted 21.02.24; published 19.03.24.

©Daemin Park, Dasom Kim, Ah-hyun Park. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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The Evolution of Nursing Research

Jacqueline m. stolley.

Trinity College of Nursing, Moline, Illinois

THE RESEARCH CULTURE in nursing has evolved in the last 150 years, beginning with Nightingale’s work in the mid-1850s and culminating in the creation of the National Institute of Nursing Research (NINR) at the National Institues of Health (NIH). This article highlights nursing’s efforts to facilitate the growth of the research culture by developing theory, establishing the importance of a research-based practice, advancing education, and providing avenues for dissemination of research. Similarities with the chiropractic profession are discussed, along with a commentary by Cheryl Hawk, D.C, Ph.D.

EVOLUTION OF NURSING RESEARCH

The development of a research culture in nursing in many ways parallels that of chiropractic, and by reviewing key aspects of the evolution of the science of nursing, there are lessons to be learned, and mistakes to be avoided. Nursing research has changed dramatically in the past 150 years, beginning with Florence Nightingale in the 19th century. Clearly, nursing research has not always had the influence and significance it holds today. In fact, for a number of years after Nightingale’s work, little is found in the literature concerning nursing research. This is perhaps due to the past perception of nursing as an apprenticeship in a task-oriented caring profession ( 1 ). Although research was conducted with respect to nursing education and administration in the first half of the 20th century, it was not until the 1950s that nursing research began the advancement that has been seen in the past three decades. This is due to many factors: an increase in the number of nurses with advanced academic preparation, the establishment of vehicles for dissemination of nursing research, federal funding and support for nursing research, and the upgrading of research skills in faculty and students. This article provides a brief review of the development of research in nursing, and along with it, the theory that has guided that process.

NURSING THEORY DEVELOPMENT

As with other practice professions, nursing requires a knowledge foundation that is based on theory and derived from systematic research. The first nursing theorist, Florence Nightingale, created detailed reports of both medical and nursing matters as chief nurse for the British in the Crimean War in the mid-1850s. Nightingale noted that “… apprehension, uncertainty, waiting, expectation, fear of surprise, do a patient more harm than any exertion” (p. 6) ( 2 ). As a result, Nightingale’s conceptualization of nursing included the need to have an understanding of the laws of nature, the prevention of disease, and the use of personal power. She viewed persons as both physical and spiritual beings, emphasizing the importance of the environment and the need to care for the patient, not the disease. With her emphasis on the environment, changes in nutrition, hydration, and sanitation resulted, and mortality rates dropped drastically during the Crimean War ( 3 ). In subsequent years, Nightingale developed “laws of nursing” that formed the basis for nursing science and guided nursing education in the United States from 1850 to the 1950s ( 4 ).

In the 1960s, nursing theory was used to guide teaching rather than research or practice. This was a natural outgrowth of nursing’s earlier focus on education and professional identity. Additionally, the National League for Nursing (the professional accrediting body) stipulated a conceptual framework for curriculum. Paradigmatic concepts integral to nursing were identified as person, environment, health , and nursing ( 5 – 7 ), and scientific energies were spent developing curriculum that corresponded to existing theories ( 7 ). At this point in time, nurse educators began to urge students to “care for the whole person” and textbooks underscored the importance of “holism” in nursing, with subtitles such as “The Biopsychosocial Approach.” Nurse authors acknowledged multiple causality in human illness, but all too often research, curricula, and textbooks reflected linear cause-and-effect models rather than multivariate approaches.

The 1950s and 1960s saw the development of theories explaining the art and science of nursing. Hildegard Peplau published Interpersonal Relations in Nursing ( 8 ) in 1952, based on her work as a psychiatric nurse. Other theories. included Levine’s Conservation Principles of Nursing ( 9 ) in, 1967; Roger’s Introduction to the Theoretical Basis of Nursing ( 10 ) in 1970, and The Science of Unitary Man ( 11 ) in 1980, followed by The Science of Unitary Human Beings, a Paradigm for Nursing ( 12 ) in 1983. Imogene King published A Theory for Nursing: Systems, Concepts, Process ( 13 ) in 1981, and Sister Calista Roy published her adaptation model ( 14 ) in 1980. These “grand” theories were complex and key concepts were hard to measure empirically. Thus it was difficult to test these early nursing theories through research. With the emphasis on clinical nursing research, the recent trend has been to develop and test midrange theories that describe patient problems and nursing practice.

NURSING RESEARCH DEVELOPMENT

In 1859, Nightingale used the battlefield hospitals of the Crimean War as her research laboratory, using an epidemiological process to describe the morbidity and mortality of sick and injured soldiers. Her pioneering epidemiological research and statistical methodology (documenting the relationship between the environment and health status of soldiers) was the hallmark of scientific investigation in nursing ( 4 , 15 ).

An historical review from 1900 to 1949 reveals that nursing research in the United States (see Table 1 ) was in its infancy, focusing on nursing education, nurses, nursing students, and ways to organize nurses’ work. As noted earlier, at this time, nursing theory was discussed solely as a means of developing and organizing educational curriculum. Early educators were unable to develop educational programs that both represented a nursing perspective and helped students focus on nursing concepts and problems rather than medical concepts and problems. In the first half of this century, groups were formed to answer such questions as: what is nursing, what do nurses do, and how unique is nursing from other health science disciplines? Professional debates raged as to whether nursing was merely a “poor stepsister” of medicine or whether it was part of the biological, natural, or physical sciences ( 4 ). Research during this period was essentially nonexistent in terms of nursing practice.

Nursing: Historical Developments in Nursing Theory and Research

It was not until the 1980s that nursing devoted a sizeable portion of its research effort to patients and patient behavior, an emphasis that emerged logically as nurses began to recognize the interplay between behavior and rehabilitation or recovery from illness. Historically, nurses searched for single causative agents when promoting health or preventing illness, even as they acknowledged the contributions of multiple other factors. Predominant modes of inquiry relied on early in the development of a culture of nursing research were empirical (logical positivist). Nurse researchers modeled themselves after colleagues in the basic and biomedical sciences, perhaps in an effort to seek scientific validation. Only during the 1980s and 1990s did nurses increasingly use qualitative research methods, such as phenomenology and ethnography, to explain complex human phenomena. Therefore, nurse researchers are just beginning to respond to the need to view human problems in less reductionistic terms when the research questions call for a holistic combination of quantitative and qualitative research methodologies ( 16 ). Over the past two decades, many nurses have pursued further education, consultation, or research to enhance their understanding and ability to respond constructively to patient behavior. For example, by the mid-1980s, there was a sizable increase in nursing studies of individuals and families experiencing developmental, environmental, or illness-generated crisis situations involving both acute and long-term stress responses ( 17 – 20 ).

During the 1990s, nursing practice underwent a clinical revolution in response to societal, medical, scientific, and technologic advances. Changes in nursing practice began to result from nursing research (e.g., research-based practice guidelines) as the efforts of individuals both in and outside of nursing (e.g., National Academy of Science, National Institute for Nursing Research, and major foundations) coalesced to stimulate and support clinical nursing research. Concurrently, there was a new surge of interest among nurses themselves in redefining the problems of their practice and delineating the gaps in knowledge underpinning their practice base. As noted earlier, the current decade has been marked by interest in multiple modes of inquiry (qualitative and quantitative) for a practice discipline which must address complex human phenomena. In the past, the type of research questions most often addressed through nursing research were of a descriptive or exploratory nature. However, nurse researchers are now going beyond “what is” and “how” questions and are addressing more explanatory or predictive-level questions using methodologically rigorous experimental and quasi-experimental designs as they redefine clinical problems and systematically address gaps in their knowledge base. After becoming established in the research arena, nursing researchers have expanded to incorporate and collaborate on interdisciplinary studies, health care systems and health services research, and taxonomies such as Nursing Intervention Classification (NIC) ( 21 ) and Nursing Outcomes Classification (NOC) ( 22 ). The taxonomies represent efforts to define what nurses do and outcomes sensitive to nursing interventions.

The culture of nursing research has now advanced to the point where consideration can be given not just to the conduct of research, but also to its application in practice. The conduct of research is not the end, but rather a means through which practice is improved by utilizing research findings. Research utilization is the process of conveying and applying research-based knowledge to impact or change existing practices in the health care system ( 23 ). The primary components of research utilization involve summarizing knowledge generated through research; imparting the research knowledge to nurses, other health professionals, policymakers, and consumers of health care; and accomplishing desired outcomes for patients, their families, and health care providers and agencies. Models for research utilization were developed in the 1970s, beginning with the Western Interstate Commission for Higher Education in Nursing (WCHEN) Regional Program for Nursing Development ( 24 ). Other models include the Conduct and Utilization of Research in Nursing (CURN) project ( 25 ), the Stetler/Marram model ( 26 ), the retrieval and application of research in nursing (RARIN) model ( 27 ) and the Iowa model of research in practice ( 28 ). The primary goal of research utilization programs is to make research findings an integral part of nursing practice, assuring research-based care delivery models. Research utilization is an excellent model for application of research findings to practice by advanced practice nurses.

An important trend is the use of research findings to serve as the basis for treatment decision making called evidence-based practice ( 29 ). Using this process, a question involving treatment is developed, and determination of the adequacy of current research is made. If the research base is adequate, it is synthesized, protocols are developed and applied, and evaluation is completed. Through these efforts, the nursing profession, in partnership with other professions, bridges the gap between research and practice to improve patient care.

EDUCATIONAL ADVANCEMENT

Early nursing education took place in hospital training programs (nursing diploma), modeled on Florence Nightingale’s work in the United Kingdom ( 30 ). In 1915, nursing’s educational accrediting body, the National League for Nursing (NLN) called for university-level education. Baccalaureate programs in nursing emerged in 1923 at Yale University and Western Reserve University, but the majority of nursing education took place in hospital-based diploma programs. In 1971, the first community college programs for nursing education opened, providing graduates with an associate degree in nursing. Today, entry into nursing practice takes place primarily in associate degree programs, with baccalaureate programs second. Gradually, diploma programs have decreased in number, and few exist today. Associate degree programs may introduce nursing students to research, but baccalaureate programs included nursing research in the upper division curriculum. From 1900 to the 1960s, most nursing leaders obtained their graduate-level preparation in schools of education ( 30 ). For many years, the Master’s degree was considered the terminal degree in nursing.

The number of nurses whose career was devoted to research was miniscule in the 1960s. Indeed, even by the 1970s only about 400 nurses in the United States held a doctoral degree ( 31 ). In 1955, the Nursing Research Grants and Fellowship Program of the Division of Nursing, United States Public Health Service (USPHS) was established. This program awarded grants for nursing research projects, nursing research fellowships, and nurse-scientist graduate training ( 32 ). Early funding was for nurses to obtain their doctorates in fields outside of nursing, because there were no nursing doctoral programs available. As a result, nurse-physiologists, nurse-anthropologists, and so forth emerged in the field. They were educated to conduct research, but often stayed in the field of their doctorate and did not apply their research efforts to nursing care problems.The emphasis during this period continued to be on establishing nursing’s rightful place in the academic setting of the university. As nursing became integrated into university life during the 1970s, nursing faculty became aware of their responsibility to develop new knowledge, and in many university-based schools of nursing, faculty members began to prepare both themselves and their students to become investigators ( 33 ).

Currently, three types of doctoral degrees in nursing are available. A Nursing Doctorate (ND), first established at Case Western Reserve University in 1979, was designed to be equivalent to the Doctor of Medicine degree, providing students preparation for the practice of generalized nursing and future leadership, but not for advanced practice. Professional doctorates, Doctor of Nursing Science (DNS, DNSc, DSN), emphasized advanced clinical, administrative, or policy-related practice and leadership. The Doctor of Nursing Science degree focuses on applied rather than basic research, and on applying and testing new knowledge in practice. Although the Doctor of Philosophy (PhD) was first available to nurses at Teachers College at Columbia University in the 1920s, interest in doctoral education was rekindled in the 1970s. The number of doctoral programs in nursing has increased from zero in the 1950s to over 65 institutions, three-fourths of which are academic doctorates (PhD) that prepare graduates for a lifetime of scholarship and research ( 34 ). More recently, nurses in academic settings have been encouraged to obtain postdoctoral research training with support available through both individual (F32) and institutional (T32) traineeships through the National Institutes of Health (NIH).

RESEARCH FUNDING AND SUPPORT

Several factors stimulated the growth of nursing research in the 1980s and 1990s. Perhaps the most important factor was the creation in 1986 of the National Center for Nursing Research (NCNR) in the United States Public Health Service (USPHS). The development of this Center resulted from intense political action by the American Nurses’ Association (ANA) ( 23 ). The primary aim of the NCNR was “the conduct, support, and dissemination of information regarding basic and clinical nursing research, training and other programs in patient care research” (p. 2) ( 35 ). Prior to the establishment of the NCNR, most of the federal funds supporting research were designated for medical studies that concentrated on the diagnosis and cure of disease. Thus, creation of the NCNR was a major achievement for nurse researchers. In 1993, the NCNR became the National Institute of Nursing Research (NINR), strengthening nursing’s position by giving the Center institute status within the NIH. This advance served to put nursing into the mainstream of research activities and on more equal status with scientists and other health professions. With the establishment of the Center and then the Institute, federal funding for nursing research has grown. In 1986, the NCNR had a budget of $16.2 million. In 1996, the budget for the NINR was about $55 million ( 1 ), more than a threefold increase over a decade. The NINR elected to foster five research priorities for 1995 through 1999: community-based nursing models, effectiveness of nursing interventions in HIV/AIDS, cognitive impairment, living with chronic illness, and biobehavioral factors related to immunocompetence ( 22 ).

The NINR’s strategic plan for the next millenium includes funding nursing research on chronic illnesses (e.g., improving adherence to chemotherapy, pain relief), quality and cost effectiveness of care, health promotion and disease prevention, management of symptoms (e.g., gender differences in response to therapeutics, managing the pain cycle), health disparities (e.g., cultural sensitivity), adaptation to new technologies (e.g., transplants), and palliative care at the end of life. Special allocations and Requests for Applications (RFA) have facilitated research in these target areas, although investigator-initiated research topics are funded if they are significant to nursing or patient care. The projected budget for NINR for the year 2000 is over $70 million, which is approximately distributed as follows: 73% for extramural research project grants; 8% for pre- and postdoctoral training; 3.5% for career development; 3.5% for Core Centers in specialized areas of research inquiry; 3% for the intramural program. Planning research for the next 5 years and into the next century is a welcome challenge for the NINR and the scientific community ( 36 ).

RESEARCH DISSEMINATION

Significant milestones in the development of nursing science began in the mid-1950s (see Table 1 ). From 1950 to 1959, there was growing emphasis on the need to identify a body of knowledge for the developing profession of nursing in order to justify its presence in post-World War II universities ( 37 ). Not only was the first journal of Nursing Research established in 1952; several textbooks related to nursing research were also published. Another critical step in the evolution of the culture of nursing science was the establishment of the American Nurses Foundation by the American Nurses Association specifically to promote nursing research. During the 1950s, regional research conferences were instituted for the first time, and federal support of nursing research began ( 4 , 33 , 37 ). All these elements were essential to the development of a science of nursing.

Several new nursing research journals, including Applied Nursing Research (ANR), were instituted in the late 1980s. ANR publishes research reports of special significance to nurse clinicians ( 1 ). Increasingly, clinical specialty (i.e., Heart and Lung, Journal of Gerontological Nursing ) journals are publishing data-based articles as well.

Another important event in the development and dissemination of nursing theory and research was the creation of the Annual Review of Nursing Research in 1983. This publication includes critical analyses of research pertinent to nursing and health, including nursing practice, nursing care delivery, nursing education, and the nursing profession. Chapters systematically assess knowledge development in nursing, encourage the use of research findings in practice, and provide direction for future research ( 22 ). More recently, scholars have joined to create the Encyclopedia of Nursing Research ( 38 ), a publication that provides a comprehensive overview of research studies, the history of nursing research, and the evolution of theory development in nursing.

The next century challenges nursing research with critical imperatives for improving health care. Changes in our nation’s population and their needs and expectations will impact the direction of nursing research. Consumers are becoming more involved in managing their own health care, and practitioners are continually adjusting to new technologies as well as innovative health care systems. The broad spectrum of nursing research encompasses both clinical and basic investigations with the patient as the central focus. Nursing must concentrate on making certain that our valuable scientific findings are incorporated into practice and focus on developing the next generation of nurse researchers ( 36 ).

The research culture in chiropractic is similar to where nursing research was in its early years. To move chiropractic research forward will require many of the same changes that occurred in nursing, such as educational advancement, collaboration in academic settings, federal acknowledgement and support, and development of more avenues for research dissemination to practitioners. Just as nursing had to overcome significant barriers such as attitudes and low educational and professional status, so, too, will the chiropractic profession have to strive to develop a research tradition in order to integrate research as part of its practice culture.

This paper is in the following e-collection/theme issue:

Published on 18.3.2024 in Vol 10 (2024)

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Authors of this article:

Author Orcid Image

Original Paper

  • Nicole L Vike 1 , PhD   ; 
  • Sumra Bari 1 , PhD   ; 
  • Leandros Stefanopoulos 2, 3 * , MSc   ; 
  • Shamal Lalvani 2 * , MSc   ; 
  • Byoung Woo Kim 1 * , MSc   ; 
  • Nicos Maglaveras 3 , PhD   ; 
  • Martin Block 4 , PhD   ; 
  • Hans C Breiter 1, 5 , MD   ; 
  • Aggelos K Katsaggelos 2, 6, 7 , PhD  

1 Department of Computer Science, University of Cincinnati, Cincinnati, OH, United States

2 Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States

3 School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece

4 Integrated Marketing Communications, Medill School, Northwestern University, Evanston, IL, United States

5 Department of Psychiatry, Massachusetts General Hospital, Harvard School of Medicine, Boston, MA, United States

6 Department of Computer Science, Northwestern University, Evanston, IL, United States

7 Department of Radiology, Northwestern University, Evanston, IL, United States

*these authors contributed equally

Corresponding Author:

Hans C Breiter, MD

Department of Computer Science

University of Cincinnati

2901 Woodside Drive

Cincinnati, OH, 45219

United States

Phone: 1 617 413 0953

Email: [email protected]

Background: Despite COVID-19 vaccine mandates, many chose to forgo vaccination, raising questions about the psychology underlying how judgment affects these choices. Research shows that reward and aversion judgments are important for vaccination choice; however, no studies have integrated such cognitive science with machine learning to predict COVID-19 vaccine uptake .

Objective: This study aims to determine the predictive power of a small but interpretable set of judgment variables using 3 machine learning algorithms to predict COVID-19 vaccine uptake and interpret what profile of judgment variables was important for prediction.

Methods: We surveyed 3476 adults across the United States in December 2021. Participants answered demographic, COVID-19 vaccine uptake (ie, whether participants were fully vaccinated), and COVID-19 precaution questions. Participants also completed a picture-rating task using images from the International Affective Picture System. Images were rated on a Likert-type scale to calibrate the degree of liking and disliking. Ratings were computationally modeled using relative preference theory to produce a set of graphs for each participant (minimum R 2 >0.8). In total, 15 judgment features were extracted from these graphs, 2 being analogous to risk and loss aversion from behavioral economics. These judgment variables, along with demographics, were compared between those who were fully vaccinated and those who were not. In total, 3 machine learning approaches (random forest, balanced random forest [BRF], and logistic regression) were used to test how well judgment, demographic, and COVID-19 precaution variables predicted vaccine uptake . Mediation and moderation were implemented to assess statistical mechanisms underlying successful prediction.

Results: Age, income, marital status, employment status, ethnicity, educational level, and sex differed by vaccine uptake (Wilcoxon rank sum and chi-square P <.001). Most judgment variables also differed by vaccine uptake (Wilcoxon rank sum P <.05). A similar area under the receiver operating characteristic curve (AUROC) was achieved by the 3 machine learning frameworks, although random forest and logistic regression produced specificities between 30% and 38% (vs 74.2% for BRF), indicating a lower performance in predicting unvaccinated participants. BRF achieved high precision (87.8%) and AUROC (79%) with moderate to high accuracy (70.8%) and balanced recall (69.6%) and specificity (74.2%). It should be noted that, for BRF, the negative predictive value was <50% despite good specificity. For BRF and random forest, 63% to 75% of the feature importance came from the 15 judgment variables. Furthermore, age, income, and educational level mediated relationships between judgment variables and vaccine uptake .

Conclusions: The findings demonstrate the underlying importance of judgment variables for vaccine choice and uptake, suggesting that vaccine education and messaging might target varying judgment profiles to improve uptake. These methods could also be used to aid vaccine rollouts and health care preparedness by providing location-specific details (eg, identifying areas that may experience low vaccination and high hospitalization).

Introduction

In early 2020, the COVID-19 pandemic wreaked havoc worldwide, triggering rapid vaccine development efforts. Despite federal, state, and workplace vaccination mandates, many individuals made judgments against COVID-19 vaccination, leading researchers to study the psychology underlying individual vaccination preferences and what might differentiate the framework for judgment between individuals who were fully vaccinated against COVID-19 and those who were not (henceforth referred to as vaccine uptake ). A better understanding of these differences in judgment may highlight targets for public messaging and education to increase the incidence of choosing vaccination.

Multiple studies have sought to predict an individual’s intention to receive a COVID-19 vaccine or specific variables underlying vaccination choices or mitigation strategies [ 1 - 7 ], but few have predicted vaccine uptake . One such study used 83 sociodemographic variables (with education, ethnicity, internet access, income, longitude, and latitude being the most important predictors) to predict vaccine uptake with 62% accuracy [ 8 ], confirming both the importance and limitations of these variables in prediction models. Other studies have compared demographic groups between vaccinated and nonvaccinated persons; Bulusu et al [ 9 ] found that young adults (aged 18-35 years), women, and those with higher levels of education had higher odds of being vaccinated. In a study of >12 million persons, the largest percentage of those who initiated COVID-19 vaccination were White, non-Hispanic women between the ages of 50 and 64 years [ 10 ]. Demographic variables are known to affect how individuals judge what is rewarding or aversive [ 11 , 12 ] yet are not themselves variables quantifying how individuals make judgments that then frame decisions.

Judgment reflects an individual’s preferences, or the variable extent to which they approach or avoid events in the world based on the rewarding or aversive effects of these events [ 13 - 15 ]. The definition of preference in psychology differs from that in economics. In psychology, preferences are associated with “wanting” and “liking” and are framed by judgments that precede decisions, which can be quantified through reinforcement reward or incentive reward tasks [ 12 , 16 - 21 ]. In economics, preferences are relations derived from consumer choice data (refer to the axioms of revealed preference [ 22 ]) and reflect choices or decisions based on judgments that place value on behavioral options. Economist Paul Samuelson noted that decisions are “assumed to be correlative to desire or want” [ 23 ]. In this study, we focused on a set of variables that frame judgment, with the presumption that judgments precede choices [ 12 , 20 ]. Variables that frame judgment can be derived from tasks using operant key-pressing tasks that quantify “wanting” [ 24 - 33 ] or simple rating tasks that are analogous to “liking” [ 20 , 34 ]. Both operant keypress and rating tasks measure variables that quantify the average (mean) magnitude ( K ), variance ( σ ), and pattern (ie, Shannon entropy [ H ]) of reward and aversion judgments [ 35 ]. We refer to this methodology and the multiple relationships between these variables and features based on their graphical relationships as relative preference theory (RPT; Figure 1 ) [ 18 , 36 ]. RPT has been shown to produce discrete, recurrent, robust, and scalable relationships between judgment variables [ 37 ] that produce mechanistic models for prediction [ 33 ], and which have demonstrated relationships to brain circuitry [ 24 - 27 , 30 ] and psychiatric illness [ 28 ]. Of the graphs produced for RPT, 2 appear to resemble graphs derived with different variables in economics, namely, prospect theory [ 38 ] and the mean-variance function for portfolio theory described by Markowitz [ 39 ]. Given this graphical resemblance, it is important to note that RPT functions quantifying value are not the same as standard representations of preference in economics. Behavioral economic variables such as loss aversion and risk aversion [ 38 , 40 - 51 ] are not to be interpreted in the same context given that both reflect biases and bounds to human rationality. In psychology, they are grounded in judgments that precede decisions, whereas in economics, they are grounded in consumer decisions themselves. Going forward, we will focus on judgment-based loss aversion, representing the overweighting of negative judgments relative to positive ones, and judgment-based risk aversion, representing the preference for small but certain assessments over larger but less certain ones (ie, assessments that have more variance associated with them) [ 38 , 40 - 51 ]. Herein, loss aversion and risk aversion refer to ratings or judgments that precede decisions.

A number of studies have described how risk aversion and other judgment variables are important for individual vaccine choices and hesitancies [ 52 - 58 ]. Hudson and Montelpare [ 54 ] found that risk aversion may promote vaccine adherence when people perceive contracting a disease as more dangerous or likely. Trueblood et al [ 52 ] noticed that those who were more risk seeking (as measured via a gamble ladder task) were more likely to receive the vaccine even if the vaccine was described as expedited. Wagner et al [ 53 ] described how risk misperceptions (when the actual risk does not align with the perceived risk) may result from a combination of cognitive biases, including loss aversion. A complex theoretical model using historical vaccine attitudes grounded in decision-making has also been proposed to predict COVID-19 vaccination, but this model has not yet been tested [ 59 ]. To our knowledge, no study has assessed how well a model comprising variables that reflect reward and aversion judgments predicts vaccine uptake .

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Goal of This Study

Given the many vaccine-related issues that occurred during the COVID-19 pandemic (eg, vaccine shortages, hospital overload, and vaccination resistance or hesitancy), it is critical to develop methods that might improve planning around such shortcomings. Because judgment variables are fundamental to vaccine choice, they provide a viable target for predicting vaccine uptake . In addition, the rating methodology used to quantify variables of judgment is independent of methods quantifying vaccine uptake or intent to vaccinate, limiting response biases within the study data.

In this study, we aimed to predict COVID-19 vaccine uptake using judgment, demographic, and COVID-19 precaution (ie, behaviors minimizing potential exposure to COVID-19) variables using multiple machine learning algorithms, including logistic regression, random forest, and balanced random forest (BRF). BRF was hypothesized to perform best given its potential benefits with handling class imbalances [ 60 ], balancing both recall and specificity, and producing Gini scores that provide relative variable importance to prediction. In this study, the need for data imbalance techniques was motivated by the importance of the specificity metric, which would reflect the proportion of participants who did not receive full vaccination; without balancing, the model might not achieve similar recall and specificity values. When there is a large difference between recall and specificity, specificity might instead reflect the size of the minority class (those who did not receive full vaccination). In general, random forest approaches have been reported to have benefits over other approaches such as principal component analysis and neural networks, in which the N-dimensional feature space or layers (in the case of neural networks) are complex nonlinear functions, making it difficult to interpret variable importance and relationships to the outcome variable. To provide greater certainty about these assumptions, we performed logistic regression in parallel with random forest and BRF. The 3 machine learning approaches used a small feature set (<20) with interpretable relationships to the predicted variable. Such interpretations may not be achievable in big data approaches that use hundreds to thousands of variables that seemingly add little significance to the prediction models. Interpretation was facilitated by (1) the Gini importance criterion associated with BRF and random forest, which provided a profile of the judgment variables most important for prediction; and (2) mediation and moderation analyses that offered insights into statistical mechanisms among judgment variables, demographic (contextual) variables, and vaccine uptake . Determining whether judgment variables are predictive of COVID-19 vaccine uptake and defining which demographic variables facilitate this prediction presents a number of behavioral targets for vaccine education and messaging—and potentially identifies actionable targets for increasing vaccine uptake .

More broadly, the prediction of vaccine uptake may aid (1) vaccine supply chain and administration logistics by indicating areas that may need more or fewer vaccines, (2) targeted governmental messaging to locations with low predicted uptake, and (3) preparation of areas that may experience high cases of infection that could ultimately impact health care preparedness and infrastructure. The proposed method could also be applied to other mandated or government-recommended vaccines (eg, influenza and human papillomavirus) to facilitate the aforementioned logistics. Locally, vaccine uptake prediction could facilitate local messaging and prepare health care institutions for vaccine rollout and potential hospital overload. Nationally, prediction might inform public health officials and government communication bodies that are responsible for messaging and vaccine rollout with the goal of improving vaccine uptake and limiting infection and hospital overload.

Recruitment

Similar recruitment procedures for a smaller population-based study have been described previously [ 61 - 63 ]. In this study, participants were randomly sampled from the general US population using an email survey database used by Gold Research, Inc. Gold Research administered questionnaires in December 2021 using recruitment formats such as (1) customer databases from large companies that participate in revenue-sharing agreements, (2) social media, and (3) direct mail. Recruited participants followed a double opt-in consent procedure that included primary participation in the study as well as secondary use of anonymized, deidentified data (ie, all identifying information was removed by Gold Research before retrieval by the research group) in secondary analyses (refer to the Ethical Considerations section for more detail). During consent procedures, participants provided demographic information (eg, age, race, and sex) to ensure that the sampled participants adequately represented the US census at the time of the survey (December 2021). Respondents were also presented with repeated test questions to screen out those providing random and illogical responses or showing flatline or speeder behavior. Participants who provided such data were flagged, and their data were removed.

Because other components of the survey required an adequate sample of participants with mental health conditions, Gold Research oversampled 15% (60,000/400,000) of the sample for mental health conditions, and >400,000 respondents were contacted to complete the questionnaire. Gold Research estimated that, of the 400,000 participants, >300,000 (>75%) either did not respond or declined to participate. Of the remaining 25% (100,000/400,000) who clicked on the survey link, >50% (52,000/100,000) did not fully complete the questionnaire. Of the ≥48,000 participants who completed the survey (ie, ≥48,000/400,000, ≥12% of the initial pool of queried persons), those who did not clear data integrity assessments were omitted. Participants who met quality assurance procedures (refer to the following section) were selected, with a limit of 4000 to 4050 total participants.

Eligible participants were required to be aged between 18 and 70 years at the time of the survey, comprehend the English language, and have access to an electronic device (eg, laptop or smartphone).

Ethical Considerations

All participants provided informed consent, which included their primary participation in the study as well as the secondary use of their anonymized, deidentified data (ie, all identifying information removed by Gold Research before retrieval by the research group) in secondary analyses. This study was approved by the Northwestern University institutional review board (approval STU00213665) for the initial project start and later by the University of Cincinnati institutional review board (approval 2023-0164) as some Northwestern University investigators moved to the University of Cincinnati. All study approvals were in accordance with the Declaration of Helsinki. All participants were compensated with US $10 for taking part. Detailed survey instructions have been published previously [ 61 - 63 ].

Quality Assurance and Data Exclusion

Three additional quality assurance measures were used to flag nonadhering participants: (1) participants who indicated that they had ≥10 clinician-diagnosed illnesses (refer to Figure S1 in Multimedia Appendix 1 [ 18 , 33 , 36 , 64 - 68 ] for a list), (2) participants who showed minimal variance in the picture-rating task (ie, all pictures were rated the same or the ratings varied only by 1 point; refer to the Picture-Rating Task section), and (3) inconsistencies between educational level and years of education and participants who completed the questionnaire in <800 seconds.

Data from 4019 participants who passed the initial data integrity assessments were anonymized and then sent to the research team. Data were further excluded if the quantitative feature set derived from the picture-rating task was incomplete or if there were extreme outliers (refer to the RPT Framework section). Using these exclusion criteria, of the 4019 participants, 3476 (86.49%) were cleared for statistical analysis, representing 0.87% (3476/400,000) of the initial recruitment pool. A flowchart of participant exclusion is shown in Figure 2 .

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Questionnaire

Participants were asked to report their age, sex, ethnicity, annual household income, marital status, employment status, and educational level. Participants were asked to report whether they had received the full vaccination ( yes or no responses). At the time of the survey, participants were likely to have received either 2 doses of the Pfizer or Moderna vaccine or 1 dose of the Johnson & Johnson vaccine as per the Centers for Disease Control and Prevention guidelines. Participants were also asked to respond yes (they routinely followed the precaution) or no (they did not routinely follow the precaution) to 4 COVID-19 precaution behaviors: mask wearing, social distancing, washing or sanitizing hands, and not gathering in large groups (refer to Tables S1 and S2 in Multimedia Appendix 1 for the complete questions and sample sizes, respectively). In addition, participants completed a picture-rating task at 2 points during the survey (refer to the Picture-Rating Task section).

Picture-Rating Task

A picture-rating task was administered to quantify participants’ degree of liking and disliking a validated picture set using pictures calibrated over large samples for their emotional intensity and valence [ 69 , 70 ]. Ratings from this task have been mathematically modeled using RPT to define graphical features of reward and aversion judgments. Each feature quantifies a core aspect of judgment, including risk aversion and loss aversion. Judgment variables have been shown to meet the criteria for lawfulness [ 37 ] that produce mechanistic models for prediction [ 33 ], with published relationships to brain circuitry [ 24 - 27 , 30 ] and psychiatric illness [ 28 ]. A more complete description of these judgment variables and their computation can be found in the RPT Framework section and in Table 1 .

For this task, participants were shown 48 unique color images from the International Affective Picture System [ 69 , 70 ]. A total of 6 picture categories were used: sports, disasters, cute animals, aggressive animals, nature (beach vs mountains), and men and women dressed minimally, with 8 pictures per category (48 pictures in total; Figure 1 A). These images have been used and validated in research on human emotion, attention, and preferences [ 69 , 70 ]. The images were displayed on the participants’ digital devices with a maximum size of 1204 × 768 pixels. Below each picture was a rating scale from −3 ( dislike very much ) to +3 ( like very much ), where 0 indicated indifference ( Figure 1 A). While there was no time limit for selecting a picture rating, participants were asked to rate the images as quickly as possible and use their first impression. Once a rating was selected, the next image was displayed.

RPT Framework

Ratings from the picture-rating task were analyzed using an RPT framework. This framework fits approach and avoidance curves and derives mathematical features from graphical plots ( Figures 1 B-1D). These methods have been described at length in prior work and are briefly described in this section [ 11 , 18 , 33 , 36 ]. More complete descriptions and quality assurance procedures can be found in Multimedia Appendix 1 .

At least 15 judgment variables can be mathematically derived from this framework and are psychologically interpretable; they have been validated using both operant keypress [ 9 , 25 - 27 ] and picture-rating tasks [ 11 , 34 ]. The 15 judgment variables are loss aversion, risk aversion, loss resilience, ante, insurance, peak positive risk, peak negative risk, reward tipping point, aversion tipping point, total reward risk, total aversion risk, reward-aversion trade-off, trade-off range, reward-aversion consistency, and consistency range. Loss aversion, risk aversion, loss resilience, ante, and insurance are derived from the logarithmic or power-law fit of mean picture ratings ( K ) versus entropy of ratings ( H ); this is referred to as the value function ( Figure 1 B). Peak positive risk, peak negative risk, reward tipping point, aversion tipping point, total reward risk, and total aversion risk are derived from the quadratic fit of K versus the SD of picture ratings ( σ ); this is referred to as the limit function ( Figure 1 C). Risk aversion trade-off, trade-off range, risk aversion consistency, and consistency range are derived from the radial fit of the pattern of avoidance judgments ( H − ) versus the pattern of approach judgments ( H + ); this is referred to as the trade-off function ( Figure 1 D). Value (Figure S2A in Multimedia Appendix 1 ), limit (Figure S2B in Multimedia Appendix 1 ), and trade-off (Figure S2C in Multimedia Appendix 1 ) functions were plotted for 500 randomly sampled participants, and nonlinear curve fits were assessed for goodness of fit, yielding R 2 , adjusted R 2 , and the associated F statistic for all participants (Figure S2D in Multimedia Appendix 1 ). Only the logarithmic and quadratic fits are listed in Table S3 in Multimedia Appendix 1 . Each feature describes a quantitative component of a participant’s reward and aversion judgment (refer to Table 1 for abbreviated descriptions and Multimedia Appendix 1 for complete descriptions). Collectively, the 15 RPT features will be henceforth referred to as “judgment variables.” The summary statistics for these variables can be found in Table S3 in Multimedia Appendix 1 .

Statistical and Machine Learning Analyses

Wilcoxon rank sum tests, chi-square tests, and Gini importance plotting were performed in Stata (version 17; StataCorp) [ 72 ]. Machine learning algorithms were run in Python (version 3.9; Python Software Foundation) [ 73 ], where the scikit-learn (version 1.2.2) [ 74 ] and imbalanced-learn (version 0.10.1) [ 75 ] libraries were used. Post hoc mediation and moderation analyses were performed in R (version 4.2.0; R Foundation for Statistical Computing) [ 76 ].

Demographic and Judgment Variable Differences by Vaccination Uptake

Each of the 7 demographic variables (age, income, marital status, employment status, ethnicity, educational level, and sex) was assessed for differences using yes or no responses to receiving the full COVID-19 vaccination (2525/3476, 72.64% yes responses and 951/3476, 27.36% no responses), henceforth referred to as vaccine uptake . Ordinal (income and educational level) and continuous (age) demographic variables were analyzed using the Wilcoxon rank sum test ( α =.05). Expected and actual rank sums were reported using Wilcoxon rank sum tests. Nominal variables were analyzed using the chi-square test ( α =.05). For significant chi-square results, demographic response percentages were computed to compare the fully vaccinated and not fully vaccinated groups.

Each of the 15 judgment variables was assessed for differences across yes or no responses to vaccine uptake using the Wilcoxon rank sum test ( α =.05). The expected and actual rank sums were reported. Significant results ( α <.05) were corrected for multiple comparisons using the Benjamini-Hochberg correction, and Q values of <0.05 ( Q Hoch ) were reported.

Prediction Analyses

Logistic regression, random forest, and BRF were used to predict vaccine uptake using judgment, demographic, and COVID-19 precaution variables. Gini plots were produced for random forest and BRF to determine the importance of the judgment variables in predicting COVID-19 vaccination. The BRF algorithm balances the samples by randomly downsampling the majority class at each bootstrapped iteration to match the number of samples in the minority class. To provide greater certainty about the results, random forest and logistic regression were performed to compare with BRF results.

Two sets of BRF, random forest, and logistic regression analyses were run: (1) with the 7 demographic variables and 15 judgment variables included as predictors and (2) with the 7 demographic variables, 15 judgment variables, and 4 COVID-19 precaution behaviors included as predictors. COVID-19 precaution behaviors included yes or no responses to wearing a mask, social distancing, washing hands, and avoiding large gatherings (refer to Table S1 in Multimedia Appendix 1 for more details). The sample sizes for yes or no responses to the COVID-19 precaution behavior questions are provided in Table S2 in Multimedia Appendix 1 . For all 3 models, 10-fold cross-validation was repeated 100 times to obtain performance metrics, where data were split for training (90%) and testing (10%) for each of the 10 iterations in cross-validation. The averages of the performance metrics were reported across 100 repeats of 10-fold cross-validation for the test sets. The reported metrics included accuracy, recall, specificity, negative predictive value (NPV), precision, and area under the receiver operating characteristic curve (AUROC). For BRF, the Python toolbox imbalanced-learn was used to build the classifier, where the training set for each iteration of cross-validation was downsampled but the testing set was unchanged (ie, imbalanced). That is, downsampling only occurred with the bootstrapped samples for training the model, and balancing was not performed on the testing set. The default number of estimators was 100, and the default number of tree splits was 10; the splits were created using the Gini criterion. In separate analyses, estimators were increased to 300, and splits were increased to 15 to test model performance. Using the scikit-learn library, the same procedures used for BRF were followed for random forest without downsampling. Logistic regression without downsampling was implemented with a maximum of 100 iterations and optimization using a limited-memory Broyden-Fletcher-Goldfarb-Shanno solver. For logistic regression, model coefficients with respective SEs, z statistics, P values, and 95% CIs were reported.

Relative feature importance based on the Gini criterion (henceforth referred to as Gini importance ) was determined from BRF and random forest using the .feature_importances_ attribute from scikit-learn, and results were reported as the mean decrease in the Gini score and plotted in Stata. To test model performance using only the top predictors, two additional sets of BRF analyses were run: (1) with the top 3 features as predictors and (2) with the top 3 features and 15 judgment variables as predictors.

Post Hoc Mediation and Moderation

Given the importance of both judgment variables and demographic variables (refer to the Results section), we evaluated post hoc how age, income, and educational level (ie, the top 3 predictors) might statistically influence the relationship between the 15 judgment variables and COVID-19 vaccine uptake . To identify statistical mechanisms influencing our prediction results, we used mediation and moderation, which can (1) determine the directionality between variables and (2) assess variable influence in statistical relationships. Mediation is used to determine whether one variable, the mediator, statistically improves the relationship between 2 other variables (independent variables [IVs] and dependent variables [DVs]) [ 77 - 80 ]. When mediating variables improve a relationship, the mediator is said to sit in the statistical pathway between the IVs and DVs [ 77 , 80 , 81 ]. Moderation is used to test whether the interaction between an IV and a moderating variable predicts a DV [ 81 , 82 ].

For mediation, primary and secondary mediations were performed. Primary mediations included each of the 15 judgment behaviors as the IV, each of the 3 demographic variables (age, income, and educational level) as the mediator, and vaccine uptake as the DV. Secondary mediations held the 15 judgment behaviors as the mediator, the 3 demographic variables as the IV, and vaccine uptake as the DV. For moderation, the moderating variable was each of the 3 demographic variables (age, income, and educational level), the IV was each of the 15 judgment behaviors, and the DV was vaccine uptake . The mathematical procedures for mediation and moderation can be found in Multimedia Appendix 1 .

Demographic Assessment

Of the 400,000 persons queried by Gold Research, Inc, 48,000 (12%) completed the survey, and 3476 (0.87%) survived all quality assurance procedures. Participants were predominately female, married, and White individuals; employed full time with some college education; and middle-aged (mean age 51.40, SD 14.92 years; Table 2 ). Of the 3476 participants, 2525 (72.64%) reported receiving a full dose of a COVID-19 vaccine, and 951 (27.36%) reported not receiving a full dose. Participants who indicated full vaccination were predominately female, married, White individuals, and retired; had some college education; and were older on average (mean age 54.19, SD 14.13 years) when compared to the total cohort. Participants who indicated that they did not receive the full vaccine were also predominately female, married, and White individuals. In contrast to those who received the full vaccination, those not fully vaccinated were predominately employed full time, high school graduates, and of average age (mean age 43.98, SD 14.45 years; median age 45, IQR 32-56 years) when compared to the total cohort. Table 2 summarizes the demographic group sample size percentages for the total cohort, those fully vaccinated, and those not fully vaccinated.

When comparing percentages between vaccination groups, a higher percentage of male individuals were fully vaccinated, and a higher percentage of female individuals were not fully vaccinated ( Table 2 ). In addition, a higher percentage of married, White and Asian or Pacific Islander, and retired individuals indicated receiving the full vaccine when compared to the percentages of those who did not receive the vaccine ( Table 2 ). Conversely, a higher percentage of single, African American, and unemployed individuals indicated not receiving the full vaccine ( Table 2 ).

Analysis of Machine Learning Features

Demographic variable differences by vaccine uptake.

Age, income level, and educational level significantly differed between those who did and did not receive the vaccine (Wilcoxon rank sum test α <.05; Table 3 ). Those who indicated full vaccination were, on average, older (median age 59 y), had a higher annual household income (median reported income level US $50,000-$75,000), and had higher levels of education (the median reported educational level was a bachelor’s degree).

Chi-square tests revealed that marital status, employment status, sex, and ethnicity also varied by full vaccine uptake (chi-square α <.05; Table 3 ).

a N/A: not applicable.

Judgment Variable Differences by Vaccine Uptake

In total, 10 of the 15 judgment variables showed nominal rank differences ( α <.05), and 9 showed significant rank differences after correction for multiple comparisons ( Q Hoch <0.05) between those who indicated full vaccination and those who indicated that they did not receive the full vaccination ( Table 4 ). The 10 features included loss aversion, risk aversion, loss resilience, ante, insurance, peak positive risk, peak negative risk, total reward risk, total aversion risk, and trade-off range. Those who indicated full vaccination exhibited lower loss aversion, ante, peak positive risk, peak negative risk, total reward risk, and total aversion risk as well as higher risk aversion, loss resilience, insurance, and trade-off range when compared to the expected rank sum. Those who did not receive the full vaccination exhibited lower risk aversion, loss resilience, insurance, and trade-off range and higher loss aversion, ante, peak positive risk, peak negative risk, total reward risk, and total aversion risk when compared to the expected rank sum.

Machine Learning Results: Predicting Vaccination Uptake

Prediction results.

With the inclusion of demographic and judgment variables, the BRF classifier with the highest accuracy (68.9%) and precision (86.7%) in predicting vaccine uptake resulted when the number of estimators was set to 300 and the number of splits was set to 10 ( Table 5 ). With the addition of 4 COVID-19 precaution behaviors, the BRF classifier with the highest accuracy (70.8%) and precision (87.8%) to predict vaccine uptake occurred when the number of estimators was set to 300 and the number of splits was set to 10. It is notable that specificity was consistently >72%, precision was >86%, and the AUROC was >75% but the NPV was consistently <50%. For random forest and logistic regression, recall and accuracy values were higher than those for BRF, but specificity was always <39%, indicating a lower performance in predicting those who did not receive the vaccine. Precision was also lower, yet the AUROC was consistent with that of the BRF results.

a A total of 15 judgment variables ( Table 4 ), 7 demographic variables ( Table 3 ), and 4 COVID-19 precaution behavior (covid_beh) variables (Table S1 in Multimedia Appendix 1 ) were included in balanced random forest, random forest, and logistic regression models to predict COVID-19 vaccine uptake . We used 10-fold cross-validation, where the data were split 90-10 for each of the 10 iterations.

b NPV: negative predictive value.

c AUROC: area under the receiver operating characteristic curve.

d BRF: balanced random forest.

e N/A: not applicable.

Feature Importance for BRF and Random Forest

Regarding BRF, Gini importance was highest for age, educational level, and income in both BRF classifiers (both without [ Figures 3 A and 3B] and with [ Figures 3 C and 3D] inclusion of the COVID-19 precaution behaviors; refer to the clusters outlined in red in Figures 3 B and 3D). For both BRF classifiers, the top 3 predictors (age, income, and educational level) had a combined effect of 23.4% on the Gini importance for prediction. Following these predictors, the 15 judgment variables had similar importance scores for both BRF classifiers (range 0.037-0.049; refer to the clusters outlined in black in Figures 3 B and 3D). These 15 predictors had a combined effect of 62.9% to 68.7% on the Gini importance for prediction, indicating that judgment variables were collectively the most important for prediction outcomes. The least important features for predicting vaccination status were demographic variables regarding employment status, marital status, ethnicity, sex, and the 4 COVID-19 precaution behaviors. These predictors only contributed 7.3% to the Gini importance for prediction. As a follow-up analysis, BRF analyses were run using the top 3 features from both the Gini importance plots (age, educational level, and income; Table S4 in Multimedia Appendix 1 ) and the top 3 features plus 15 judgment variables (Table S5 in Multimedia Appendix 1 ). The results did not outperform those presented in Table 5 .

For random forest, the Gini importance was highest for age and educational level ( Figure 4 ). These top 2 predictors had a combined effect of 16.5% to 16.8% for the 2 models ( Figures 4 A and 4C). Following these predictors, the 15 judgment variables and the income variable had similar Gini importance, with a combined effect of 69.4% to 75.5% for Gini importance. The least important predictors mirrored those of the BRF results.

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Logistic Regression Model Statistics

Both model 1 (demographic and judgment variables) and model 2 (demographic, judgment, and COVID-19 precaution behavior variables) were significant ( P <.001). The model statistics are provided in Tables 6 (model 1) and 7 (model 2). In model 1, age, income, marital status, employment status, sex, educational level, ante, aversion tipping point, reward-aversion consistency, and consistency range were significant ( α <.05). In model 2, age, income, marital status, employment status, sex, educational level, risk aversion, ante, peak negative risk, mask wearing, and not gathering in large groups were significant ( α <.05).

a Overall model: P <.001; pseudo- R 2 =0.149; log-likelihood=−1736.8; log-likelihood null=−2039.7.

a Overall model: P <.001; pseudo- R 2 =0.206; log-likelihood=−1620.0; log-likelihood null=−2039.7.

Because judgment variables and demographic variables (age, income, and educational level) were important predictors, we evaluated post hoc whether demographics statistically mediated or moderated the relationship between each of the 15 judgment variables and binary responses to COVID-19 vaccination.

For primary mediations, age significantly mediated the statistical relationship between 11 judgment variables and vaccine uptake ( α <.05; Table 8 ), income mediated 8 relationships α < <.05; Table 8 ), and educational level mediated 9 relationships ( α <.05; Table 8 ). In total, 7 judgment variables overlapped across the 3 models: loss resilience, ante, insurance, peak positive risk, peak negative risk, risk aversion trade-off, and consistency range. Of these, 5 significantly differed between vaccine uptake (those fully vaccinated and those not): loss resilience, ante, insurance, peak positive risk, and peak negative risk ( Table 3 ). Thus, 2 judgment features did not differ by vaccine uptake but were connected with uptake by significant mediation.

For the secondary mediation analyses, 5 judgment variables mediated the statistical relationship between age and vaccine uptake ; these variables overlapped with the 11 findings of the primary mediation analyses. Furthermore, 4 judgment variables mediated the statistical relationship between income and vaccine uptake ; these variables overlapped with the 8 findings of the primary mediation analyses. Finally, 4 judgment variables mediated the statistical relationship between educational level and vaccine uptake ; these variables overlapped with the 9 findings of the primary mediation analyses. In all secondary analyses, approximately half of the judgment variables were involved in mediation as compared to the doubling of judgment variable numbers observed in the primary mediation analyses. In the secondary mediation analyses, the same 4 judgment variables were found in both primary and secondary mediation results, indicating a mixed mediation framework.

From the moderation analyses, only 2 interactions out of a potential 45 were observed. Age interacted with risk aversion trade-off, and income interacted with loss resilience to statistically predict vaccine uptake ( α <.05; Table 8 ). The 2 moderation results overlapped with the mediation results, indicating mixed mediation-moderation relationships [ 78 , 80 , 81 ].

Principal Findings

Relatively few studies have sought to predict COVID-19 vaccine uptake using machine learning approaches [ 8 , 59 ]. Given that a small set of studies has assessed the psychological basis that may underlie vaccine uptake and choices [ 6 , 52 , 53 , 56 , 58 , 59 , 83 ], but none have used computational cognition variables based on reward and aversion judgment to predict vaccine uptake , we sought to assess whether variables quantifying human judgment predicted vaccine uptake . This study found that 7 demographic and 15 judgment variables predicted vaccine uptake with balanced and moderate recall and specificity, moderate accuracy, high AUROC, and high precision using a BRF framework. Other machine learning approaches (random forest and logistic regression) produced higher accuracies but lower specificities, indicating a lower prediction of those who did not receive the vaccine. The BRF also had challenges predicting the negative class, as demonstrated by the relatively low NPV despite having higher specificity than random forest and logistic regression. Feature importance analyses from both BRF and random forest showed that the judgment variables collectively dominated the Gini importance scores. Furthermore, demographic variables acted as statistical mediators in the relationship between judgment variables and vaccine uptake . These mediation findings support the interpretation of the machine learning results that demographic factors, together with judgment variables, predict COVID-19 vaccine uptake .

Interpretation of Judgment Differences Between Vaccinated and Nonvaccinated Individuals

Those who were fully vaccinated had lower values for loss aversion, ante, peak positive risk, peak negative risk, total reward risk, and total aversion risk, along with higher values for risk aversion, loss resilience, insurance, and trade-off range (refer to Table 1 for variable descriptions). Lower loss aversion corresponds to less overweighting of bad outcomes relative to good ones [ 84 ] and a potential willingness to obtain a vaccine with uncertain outcomes. A lower ante suggests that individuals are less willing to engage in risky behaviors surrounding potential infection, which is also consistent with the 4 other judgment variables that define relationships between risk and value (peak positive risk, peak negative risk, total reward risk, and total aversion risk). In participants who indicated full vaccination, lower peak positive risk and peak negative risk were related to individuals having a lower risk that they must overcome to make a choice to either approach or avoid, as per the decision utility equation by Markowitz [ 39 , 71 ]. The lower total reward risk and total aversion risk indicate that the interactions between reward, aversion, and the risks associated with them did not scale significantly; namely, higher reward was not associated with higher risk, and higher negative outcomes were not associated with the uncertainty of them. For these participants, the ability of the vaccine to increase the probability of health and reduce the probability of harm from illness did not have to overcome high obstacles in their vaccine choice. Higher risk aversion in vaccinated participants suggests that these participants viewed contracting COVID-19 as a larger risk and, therefore, were more likely to receive the full dose. These findings are consistent with those of a study by Lepinteur et al [ 58 ], who found that risk-averse individuals were more likely to accept the COVID-19 vaccination, indicating that the perceived risk of contracting COVID-19 was greater than any risk from the vaccine. Hudson and Montelpare [ 54 ] also found that risk aversion may promote vaccine adherence when people perceive contracting a disease as more dangerous or likely. Higher loss resilience in the vaccinated group was also consistent with the perspective that vaccination would improve their resilience and act as a form of insurance against negative consequences. The higher trade-off range suggests that vaccinated individuals have a broader portfolio of preferences and are more adaptive to bad things occurring, whereas a lower trade-off indicates a restriction in preferences and less adaptability in those who did not receive the vaccine.

Comparison of Prediction Algorithms

When testing these judgment variables (with demographic and COVID-19 precaution behavior variables) in a BRF framework to predict vaccine uptake , we observed a high AUROC of 0.79, where an AUROC of 0.8 is often the threshold for excellent model performance in machine learning [ 85 , 86 ]. The similarity of our reported recall and specificity values with the BRF suggests a balance between predicting true positives and true negatives. The high precision indicates a high certainty in predicting those who were fully vaccinated. The BRF model was successful in identifying those who received the full vaccine (positive cases; indicated by high precision and moderate recall) and those who did not (negative cases; indicated by the specificity). However, NPV was low, indicating a higher rate of false prediction of those who did not receive a full dose counterbalanced by a higher specificity that reflects a higher rate of predicting true negatives. These observations are reflected in the moderate accuracy, which measures the number of correct predictions. A comparison of random forest, logistic regression, and BRF revealed that random forest and logistic regression models produced less balance between recall (high) and specificity (low), which could be interpreted as a bias toward predicting the majority class (ie, those who received the vaccine). That being said, the NPV for BRF was lower than that for random forest and logistic regression, where a low NPV indicates a low probability that those predicted to have not received the vaccine truly did not receive the vaccine when taking both classes into account. Together, the results from all 3 machine learning approaches reveal challenges in predicting the negative class (ie, those who did not receive the vaccine). Overall, the 3 models achieved high accuracy, recall, precision, and AUROC. BRF produced a greater balance between recall and specificity, and the outcome of the worst-performing metric (ie, NPV) was still higher than the specificities for the random forest and logistic regression models.

Feature Importance

Of the 3 prediction algorithms, random forest and BRF had very similar Gini importance results, whereas logistic regression elevated most demographic variables and a minority of judgment variables. This observation could be due to the large variance in each of the judgment variables, which could present challenges for achieving a good fit with logistic regression. In contrast, the demographic and COVID-19 precaution variables had low variance and could be more easily fit in a linear model, hence their significance in the logistic regression results. In comparison to logistic regression, decision trees (eg, BRF and random forest) use variable variance as additional information to optimize classification, potentially leading to a higher importance of judgment variables over most demographic and all COVID-19 precaution variables.

Focusing on the model with balanced recall and specificity (ie, the BRF classifiers [with and without COVID-19 precaution behaviors]), the top predictors were 3 demographic variables (age, income, and educational level), with distributions that varied by vaccine uptake in manners consistent with those of other reports. Namely, older individuals, those identifying as male and White individuals, and those who indicated a higher income and educational level corresponded to those who were or intended to be vaccinated [ 2 , 5 , 87 ]. Despite their saliency, these 3 variables together only contributed 23% to the prediction, corresponding to approximately one-third of the contribution from the 15 judgment variables (63%-69%). The individual Gini importance scores for the 15 judgment variables only ranged from 0.039 to 0.049 but were the dominant set of features behind the moderate accuracy, high precision, and high AUROC. The 18% difference between the accuracy and precision measures suggests that variables other than those used in this study may improve prediction, including contextual variables that may influence vaccine choices. Variables may include political affiliation [ 7 ], longitude and latitude [ 8 ], access to the internet [ 8 ], health literacy [ 54 ], and presence of underlying conditions [ 9 ]. Future work should seek to include these types of variables.

In the second BRF classifier, the 4 COVID-19 precaution behaviors only contributed 6.6% to the prediction. This low contribution could be due to these variables being binary, unlike the other demographic variables, which included a range of categories. In addition, COVID-19 precaution behaviors are specific to the context of the COVID-19 pandemic and do not promote interpretation beyond their specific context. The 15 judgment variables represent a contrast to this as they are empirically computed from a set of functions across many picture categories. An individual with higher risk aversion will generally tolerate higher amounts of uncertainty regarding a potential upside or gain as opposed to settling for what they have. This does not depend on what stimulus category they observe or the stimulus-response condition. Instead, it is a general feature of the bounds to their judgment and is part of what behavioral economists such as Kahneman consider as bounds to human rationality [ 84 ].

Mechanistic Relationships Between Judgment and Demographic Variables

The Gini score plots were clear sigmoid-like graphs ( Figure 3 ), with only 3 of the 7 demographic variables ranking above the judgment variables. This observation was consistent in both BRF classifiers (with and without COVID-19 precaution behaviors), raising the possibility of a statistically mechanistic relationship among the top 3 demographic variables, the 15 judgment variables, and vaccine uptake . Indeed, we observed 28 primary mediation effects and 13 secondary mediation effects in contrast to 2 moderation relationships, which also happened to overlap with mediation findings, suggesting mixed mediation-moderation relationships [ 81 , 88 ]. The observation that most judgment variables were significant in mediation relationships but not in moderation relationships argues that prediction depended on the directional relationship between judgment and demographic variables to predict vaccine uptake . Furthermore, there were more significant primary mediations (when judgment variables were the IVs) compared to secondary mediations, suggesting the importance of judgment variables as IVs and demographic variables as mediators. Mathematically, judgment variables (IVs) influenced vaccine uptake (DV), and this relationship was stronger when demographic variables were added to the equation. The 13 secondary mediations all overlapped with the 28 primary mediations, where demographic variables were IVs and judgment variables were mediators, suggesting that demographic variables influenced vaccine uptake (DV) and that this relationship became stronger with the addition of judgment variables. This overlap of primary and secondary mediations for 4 of the judgment variables suggests that both judgment and demographic variables influenced the choice of being vaccinated within a mixed mediation framework because adding either one of them to the mediation model regressions made the relationships stronger [ 49 ]. The lack of moderation results and a considerable number of overlapping primary and secondary mediation results imply that the relationship between judgment variables and vaccine uptake did not depend purely on their interaction with age, income, or educational level (ie, moderation) but, instead, depended on the direct effects of these 3 demographic variables to strengthen the relationship between judgment variables and vaccine uptake . This type of analysis of statistical mechanisms is helpful for understanding contextual effects on our biases and might be important for considering how best to target or message those with higher loss aversion, ante, peak positive risk, peak negative risk, total reward risk, and total aversion risk (ie, in those who were not fully vaccinated).

Model Utility

The developed model is automatable and may have applications in public health. The picture-rating task can be deployed on any smart device or computer, making it accessible to much of the US population or regional populations. The ratings from this task can be automatically processed, and the results can be stored in local or national databases. This method of data collection is novel in that persons cannot bias their responses as the rating task has no perceivable relation to vaccination choices. Government and public health bodies can access these data to determine predicted vaccine uptake rates locally or nationally, which can be used to (1) prepare vaccine rollouts and supply chain demand, (2) prepare health care institutions in areas that may experience low vaccine adherence and potentially higher infection rates, and (3) determine which areas may need more targeted messaging to appeal to specific judgment profiles. For use case 3, messaging about infection risks or precaution behaviors could be framed to address those with lower risk aversion, who, in this study, tended to forgo vaccination. Given that such individualized data would not be available a priori, it would be more plausible to collect data from similarly sized cohorts in geographic regions of concern to obtain regional judgment behavior profiles and, thus, target use cases 1 to 3. Further development of this model with different population samples might also improve our understanding of how certain judgment variables may be targeted with different types of messaging, offering a means to potentially improve vaccine uptake . This model might also be applied to other mandated or recommended vaccines such as those for influenza or human papillomavirus, ultimately improving preparation and messaging efforts. However, future work would be needed to model these varying vaccine choices.

Given the use of demographic variables in the proposed model, specific demographic populations could be assessed or considered for messaging. If particular demographic groups are predicted to have a low vaccine uptake rate, messaging can be targeted to those specific groups. For example, we observed that a higher percentage of female individuals were not fully vaccinated when compared to male individuals. This could be related to concerns about the COVID-19 vaccine affecting fertility or pregnancy. To improve uptake in this population, scientifically backed messaging could be used to confirm the safety of the vaccine in this context. Lower rates of vaccination have been reported in Black communities, which was also observed in this study. Researchers have identified targetable issues related to this observation, which include engagement of Black faith leaders and accessibility of vaccination clinics in Black communities, to name a few [ 89 ].

In summary, this model could be used to predict vaccine uptake at the local and national levels and further assess the demographic and judgment features that may underlie these choices.

Limitations

This study has a number of limitations that should be considered. First, there are the inherent limitations of using an internet survey—namely, the uncontrolled environment in which participants provide responses. Gold Research, Inc, and the research team applied stringent exclusion criteria, including the evaluation of the judgment graphs given that random responses produce graphs with extremely low R 2 fits (eg, <0.1). This was not the case in our cohort of 3476 participants, but this cannot perfectly exclude random or erroneous responses to other questionnaire components. Second, participants with mental health conditions were oversampled to meet the criteria for other survey components not discussed in this paper. This oversampling could potentially bias the results, and future work should use a general population sample to verify these findings. Third, demographic variability and the resulting confounds are inherent in population surveys, and other demographic factors not collected in this study may be important for prediction (eg, religion and family size). Future work might consider collecting a broader array of demographic factors to investigate and include in predictive modeling. Fourth, we used a limited set of 7 demographic variables and 15 judgment variables; however, a larger set of judgment variables is potentially computable and could be considered for future studies. There is also little information on how post–COVID-19 effects, including socioeconomic effects, affect COVID-19 vaccination choices.

Conclusions

To our knowledge, there has been minimal research on how biases in human judgment might contribute to the psychology underlying individual vaccination preferences and what differentiates individuals who were fully vaccinated against COVID-19 from those who were not. This population study of several thousand participants demonstrated that a small set of demographic variables and 15 judgment variables predicted vaccine uptake with moderate to high accuracy and high precision and AUROC, although a large range of specificities was achieved depending on the classification method used. In an age of big data machine learning approaches, this study provides an option for using fewer but more interpretable variables. Age, income, and educational level were independently the most important predictors of vaccine uptake , but judgment variables collectively dominated the importance rankings and contributed almost two-thirds to the prediction of COVID-19 vaccination for the BRF and random forest models. Age, income, and educational level significantly mediated the statistical relationship between judgment variables and vaccine uptake , indicating a statistically mechanistic relationship grounding the prediction results. These findings support the hypothesis that small sets of judgment variables might provide a target for vaccine education and messaging to improve uptake. Such education and messaging might also need to consider contextual variables (ie, age, income, and educational level) that mediate the effect of judgment variables on vaccine uptake . Judgment and demographic variables can be readily collected using any digital device, including smartphones, which are accessible worldwide. Further development and use of this model could (1) improve vaccine uptake , (2) better prepare vaccine rollouts and health care institutions, (3) improve messaging efforts, and (4) have applications for other mandated or government-recommended vaccines.

Acknowledgments

The authors thank Carol Ross, Angela Braggs-Brown, Tom Talavage, Eric Nauman, and Marc Cahay at the University of Cincinnati (UC) College of Engineering and Applied Sciences, who significantly impacted the transfer of research funding to UC. Funding for this work was provided in part to HCB by the Office of Naval Research (awards N00014-21-1-2216 and N00014-23-1-2396) and to HCB from a Jim Goetz donation to the UC College of Engineering and Applied Sciences. Finally, the authors thank the anonymous reviewers for their constructive input, which substantially improved the manuscript. The opinions expressed in this paper are those of the authors and are not necessarily representative of those of their respective institutions.

Data Availability

The data set and corresponding key used in this study are available in Multimedia Appendix 2 .

Conflicts of Interest

A provisional patent has been submitted by the following authors (NLV, SB, HCB, SL, LS, and AKK): “Methods of predicting vaccine uptake,” provisional application # 63/449,460.

Supplementary material.

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Abbreviations

Edited by A Mavragani; submitted 11.04.23; peer-reviewed by ME Visier Alfonso, L Lapp; comments to author 18.05.23; revised version received 08.08.23; accepted 10.01.24; published 18.03.24.

©Nicole L Vike, Sumra Bari, Leandros Stefanopoulos, Shamal Lalvani, Byoung Woo Kim, Nicos Maglaveras, Martin Block, Hans C Breiter, Aggelos K Katsaggelos. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 18.03.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

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