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Tables in Research Paper – Types, Creating Guide and Examples

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Tables in Research Paper

Definition:

In Research Papers , Tables are a way of presenting data and information in a structured format. Tables can be used to summarize large amounts of data or to highlight important findings. They are often used in scientific or technical papers to display experimental results, statistical analyses, or other quantitative information.

Importance of Tables in Research Paper

Tables are an important component of a research paper as they provide a clear and concise presentation of data, statistics, and other information that support the research findings . Here are some reasons why tables are important in a research paper:

• Visual Representation : Tables provide a visual representation of data that is easy to understand and interpret. They help readers to quickly grasp the main points of the research findings and draw their own conclusions.
• Organize Data : Tables help to organize large amounts of data in a systematic and structured manner. This makes it easier for readers to identify patterns and trends in the data.
• Clarity and Accuracy : Tables allow researchers to present data in a clear and accurate manner. They can include precise numbers, percentages, and other information that may be difficult to convey in written form.
• Comparison: Tables allow for easy comparison between different data sets or groups. This makes it easier to identify similarities and differences, and to draw meaningful conclusions from the data.
• Efficiency: Tables allow for a more efficient use of space in the research paper. They can convey a large amount of information in a compact and concise format, which saves space and makes the research paper more readable.

Types of Tables in Research Paper

Most common Types of Tables in Research Paper are as follows:

• Descriptive tables : These tables provide a summary of the data collected in the study. They are usually used to present basic descriptive statistics such as means, medians, standard deviations, and frequencies.
• Comparative tables : These tables are used to compare the results of different groups or variables. They may be used to show the differences between two or more groups or to compare the results of different variables.
• Correlation tables: These tables are used to show the relationships between variables. They may show the correlation coefficients between variables, or they may show the results of regression analyses.
• Longitudinal tables : These tables are used to show changes in variables over time. They may show the results of repeated measures analyses or longitudinal regression analyses.
• Qualitative tables: These tables are used to summarize qualitative data such as interview transcripts or open-ended survey responses. They may present themes or categories that emerged from the data.

How to Create Tables in Research Paper

Here are the steps to create tables in a research paper:

• Plan your table: Determine the purpose of the table and the type of information you want to include. Consider the layout and format that will best convey your information.
• Choose a table format : Decide on the type of table you want to create. Common table formats include basic tables, summary tables, comparison tables, and correlation tables.
• Choose a software program : Use a spreadsheet program like Microsoft Excel or Google Sheets to create your table. These programs allow you to easily enter and manipulate data, format the table, and export it for use in your research paper.
• Input data: Enter your data into the spreadsheet program. Make sure to label each row and column clearly.
• Format the table : Apply formatting options such as font, font size, font color, cell borders, and shading to make your table more visually appealing and easier to read.
• Insert the table into your paper: Copy and paste the table into your research paper. Make sure to place the table in the appropriate location and refer to it in the text of your paper.
• Label the table: Give the table a descriptive title that clearly and accurately summarizes the contents of the table. Also, include a number and a caption that explains the table in more detail.
• Check for accuracy: Review the table for accuracy and make any necessary changes before submitting your research paper.

Examples of Tables in Research Paper

Examples of Tables in the Research Paper are as follows:

Table 1: Demographic Characteristics of Study Participants

This table shows the demographic characteristics of 200 participants in a research study. The table includes information about age, gender, and education level. The mean age of the participants was 35.2 years with a standard deviation of 8.6 years, and the age range was between 21 and 57 years. The table also shows that 46% of the participants were male and 54% were female. In terms of education, 10% of the participants had less than a high school education, 30% were high school graduates, 35% had some college education, and 25% had a bachelor’s degree or higher.

Table 2: Summary of Key Findings

This table summarizes the key findings of a study comparing three different groups on a particular variable. The table shows the mean score, standard deviation, t-value, and p-value for each group. The asterisk next to the t-value for Group 1 indicates that the difference between Group 1 and the other groups was statistically significant at p < 0.01, while the differences between Group 2 and Group 3 were not statistically significant.

Purpose of Tables in Research Paper

The primary purposes of including tables in a research paper are:

• To present data: Tables are an effective way to present large amounts of data in a clear and organized manner. Researchers can use tables to present numerical data, survey results, or other types of data that are difficult to represent in text.
• To summarize data: Tables can be used to summarize large amounts of data into a concise and easy-to-read format. Researchers can use tables to summarize the key findings of their research, such as descriptive statistics or the results of regression analyses.
• To compare data : Tables can be used to compare data across different variables or groups. Researchers can use tables to compare the characteristics of different study populations or to compare the results of different studies on the same topic.
• To enhance the readability of the paper: Tables can help to break up long sections of text and make the paper more visually appealing. By presenting data in a table, researchers can help readers to quickly identify the most important information and understand the key findings of the study.

Advantages of Tables in Research Paper

Some of the advantages of using tables in research papers include:

• Clarity : Tables can present data in a way that is easy to read and understand. They can help readers to quickly and easily identify patterns, trends, and relationships in the data.
• Efficiency: Tables can save space and reduce the need for lengthy explanations or descriptions of the data in the main body of the paper. This can make the paper more concise and easier to read.
• Organization: Tables can help to organize large amounts of data in a logical and meaningful way. This can help to reduce confusion and make it easier for readers to navigate the data.
• Comparison : Tables can be useful for comparing data across different groups, variables, or time periods. This can help to highlight similarities, differences, and changes over time.
• Visualization : Tables can also be used to visually represent data, making it easier for readers to see patterns and trends. This can be particularly useful when the data is complex or difficult to understand.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts

Tables and Figures

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Note:  This page reflects the latest version of the APA Publication Manual (i.e., APA 7), which released in October 2019. The equivalent resources for the older APA 6 style  can be found at this page  as well as at this page (our old resources covered the material on this page on two separate pages).

The purpose of tables and figures in documents is to enhance your readers' understanding of the information in the document; usually, large amounts of information can be communicated more efficiently in tables or figures. Tables are any graphic that uses a row and column structure to organize information, whereas figures include any illustration or image other than a table.

General guidelines

Visual material such as tables and figures can be used quickly and efficiently to present a large amount of information to an audience, but visuals must be used to assist communication, not to use up space, or disguise marginally significant results behind a screen of complicated statistics. Ask yourself this question first: Is the table or figure necessary? For example, it is better to present simple descriptive statistics in the text, not in a table.

Relation of Tables or Figures and Text

Because tables and figures supplement the text, refer in the text to all tables and figures used and explain what the reader should look for when using the table or figure. Focus only on the important point the reader should draw from them, and leave the details for the reader to examine on their own.

Documentation

If you are using figures, tables and/or data from other sources, be sure to gather all the information you will need to properly document your sources.

Integrity and Independence

Each table and figure must be intelligible without reference to the text, so be sure to include an explanation of every abbreviation (except the standard statistical symbols and abbreviations).

Organization, Consistency, and Coherence

Number all tables sequentially as you refer to them in the text (Table 1, Table 2, etc.), likewise for figures (Figure 1, Figure 2, etc.). Abbreviations, terminology, and probability level values must be consistent across tables and figures in the same article. Likewise, formats, titles, and headings must be consistent. Do not repeat the same data in different tables.

Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. In order for quantitative data to be presented clearly and efficiently, it must be arranged logically, e.g. data to be compared must be presented next to one another (before/after, young/old, male/female, etc.), and statistical information (means, standard deviations, N values) must be presented in separate parts of the table. If possible, use canonical forms (such as ANOVA, regression, or correlation) to communicate your data effectively.

A generic example of a table with multiple notes formatted in APA 7 style.

Elements of Tables

Number all tables with Arabic numerals sequentially. Do not use suffix letters (e.g. Table 3a, 3b, 3c); instead, combine the related tables. If the manuscript includes an appendix with tables, identify them with capital letters and Arabic numerals (e.g. Table A1, Table B2).

Like the title of the paper itself, each table must have a clear and concise title. Titles should be written in italicized title case below the table number, with a blank line between the number and the title. When appropriate, you may use the title to explain an abbreviation parenthetically.

Comparison of Median Income of Adopted Children (AC) v. Foster Children (FC)

Keep headings clear and brief. The heading should not be much wider than the widest entry in the column. Use of standard abbreviations can aid in achieving that goal. There are several types of headings:

• Stub headings describe the lefthand column, or stub column , which usually lists major independent variables.
• Column headings describe entries below them, applying to just one column.
• Column spanners are headings that describe entries below them, applying to two or more columns which each have their own column heading. Column spanners are often stacked on top of column headings and together are called decked heads .
• Table Spanners cover the entire width of the table, allowing for more divisions or combining tables with identical column headings. They are the only type of heading that may be plural.

All columns must have headings, written in sentence case and using singular language (Item rather than Items) unless referring to a group (Men, Women). Each column’s items should be parallel (i.e., every item in a column labeled “%” should be a percentage and does not require the % symbol, since it’s already indicated in the heading). Subsections within the stub column can be shown by indenting headings rather than creating new columns:

Chemical Bonds

     Ionic

     Covalent

     Metallic

The body is the main part of the table, which includes all the reported information organized in cells (intersections of rows and columns). Entries should be center aligned unless left aligning them would make them easier to read (longer entries, usually). Word entries in the body should use sentence case. Leave cells blank if the element is not applicable or if data were not obtained; use a dash in cells and a general note if it is necessary to explain why cells are blank.   In reporting the data, consistency is key: Numerals should be expressed to a consistent number of decimal places that is determined by the precision of measurement. Never change the unit of measurement or the number of decimal places in the same column.

There are three types of notes for tables: general, specific, and probability notes. All of them must be placed below the table in that order.

General  notes explain, qualify or provide information about the table as a whole. Put explanations of abbreviations, symbols, etc. here.

Example:  Note . The racial categories used by the US Census (African-American, Asian American, Latinos/-as, Native-American, and Pacific Islander) have been collapsed into the category “non-White.” E = excludes respondents who self-identified as “White” and at least one other “non-White” race.

Specific  notes explain, qualify or provide information about a particular column, row, or individual entry. To indicate specific notes, use superscript lowercase letters (e.g.  a ,  b ,  c ), and order the superscripts from left to right, top to bottom. Each table’s first footnote must be the superscript  a .

a  n = 823.  b  One participant in this group was diagnosed with schizophrenia during the survey.

Probability  notes provide the reader with the results of the tests for statistical significance. Asterisks indicate the values for which the null hypothesis is rejected, with the probability ( p value) specified in the probability note. Such notes are required only when relevant to the data in the table. Consistently use the same number of asterisks for a given alpha level throughout your paper.

* p < .05. ** p < .01. *** p < .001

If you need to distinguish between two-tailed and one-tailed tests in the same table, use asterisks for two-tailed p values and an alternate symbol (such as daggers) for one-tailed p values.

* p < .05, two-tailed. ** p < .01, two-tailed. † p <.05, one-tailed. †† p < .01, one-tailed.

Borders 

Tables should only include borders and lines that are needed for clarity (i.e., between elements of a decked head, above column spanners, separating total rows, etc.). Do not use vertical borders, and do not use borders around each cell. Spacing and strict alignment is typically enough to clarify relationships between elements.

Example of a table in the text of an APA 7 paper. Note the lack of vertical borders.

Tables from Other Sources

If using tables from an external source, copy the structure of the original exactly, and cite the source in accordance with  APA style .

Table Checklist

(Taken from the  Publication Manual of the American Psychological Association , 7th ed., Section 7.20)

• Is the table necessary?
• Does it belong in the print and electronic versions of the article, or can it go in an online supplemental file?
• Are all comparable tables presented consistently?
• Are all tables numbered with Arabic numerals in the order they are mentioned in the text? Is the table number bold and left-aligned?
• Are all tables referred to in the text?
• Is the title brief but explanatory? Is it presented in italicized title case and left-aligned?
• Does every column have a column heading? Are column headings centered?
• Are all abbreviations; special use of italics, parentheses, and dashes; and special symbols explained?
• Are the notes organized according to the convention of general, specific, probability?
• Are table borders correctly used (top and bottom of table, beneath column headings, above table spanners)?
• Does the table use correct line spacing (double for the table number, title, and notes; single, one and a half, or double for the body)?
• Are entries in the left column left-aligned beneath the centered stub heading? Are all other column headings and cell entries centered?
• Are confidence intervals reported for all major point estimates?
• Are all probability level values correctly identified, and are asterisks attached to the appropriate table entries? Is a probability level assigned the same number of asterisks in all the tables in the same document?
• If the table or its data are from another source, is the source properly cited? Is permission necessary to reproduce the table?

Figures include all graphical displays of information that are not tables. Common types include graphs, charts, drawings, maps, plots, and photos. Just like tables, figures should supplement the text and should be both understandable on their own and referenced fully in the text. This section details elements of formatting writers must use when including a figure in an APA document, gives an example of a figure formatted in APA style, and includes a checklist for formatting figures.

Preparing Figures

In preparing figures, communication and readability must be the ultimate criteria. Avoid the temptation to use the special effects available in most advanced software packages. While three-dimensional effects, shading, and layered text may look interesting to the author, overuse, inconsistent use, and misuse may distort the data, and distract or even annoy readers. Design properly done is inconspicuous, almost invisible, because it supports communication. Design improperly, or amateurishly, done draws the reader’s attention from the data, and makes him or her question the author’s credibility. Line drawings are usually a good option for readability and simplicity; for photographs, high contrast between background and focal point is important, as well as cropping out extraneous detail to help the reader focus on the important aspects of the photo.

Parts of a Figure

All figures that are part of the main text require a number using Arabic numerals (Figure 1, Figure 2, etc.). Numbers are assigned based on the order in which figures appear in the text and are bolded and left aligned.

Under the number, write the title of the figure in italicized title case. The title should be brief, clear, and explanatory, and both the title and number should be double spaced.

The image of the figure is the body, and it is positioned underneath the number and title. The image should be legible in both size and resolution; fonts should be sans serif, consistently sized, and between 8-14 pt. Title case should be used for axis labels and other headings; descriptions within figures should be in sentence case. Shading and color should be limited for clarity; use patterns along with color and check contrast between colors with free online checkers to ensure all users (people with color vision deficiencies or readers printing in grayscale, for instance) can access the content. Gridlines and 3-D effects should be avoided unless they are necessary for clarity or essential content information.

Legends, or keys, explain symbols, styles, patterns, shading, or colors in the image. Words in the legend should be in title case; legends should go within or underneath the image rather than to the side. Not all figures will require a legend.

Notes clarify the content of the figure; like tables, notes can be general, specific, or probability. General notes explain units of measurement, symbols, and abbreviations, or provide citation information. Specific notes identify specific elements using superscripts; probability notes explain statistical significance of certain values.

A generic example of a figure formatted in APA 7 style.

Figure Checklist 

(Taken from the  Publication Manual of the American Psychological Association , 7 th ed., Section 7.35)

• Is the figure necessary?
• Does the figure belong in the print and electronic versions of the article, or is it supplemental?
• Is the figure simple, clean, and free of extraneous detail?
• Is the figure title descriptive of the content of the figure? Is it written in italic title case and left aligned?
• Are all elements of the figure clearly labeled?
• Are the magnitude, scale, and direction of grid elements clearly labeled?
• Are parallel figures or equally important figures prepared according to the same scale?
• Are the figures numbered consecutively with Arabic numerals? Is the figure number bold and left aligned?
• Has the figure been formatted properly? Is the font sans serif in the image portion of the figure and between sizes 8 and 14?
• Are all abbreviations and special symbols explained?
• If the figure has a legend, does it appear within or below the image? Are the legend’s words written in title case?
• Are the figure notes in general, specific, and probability order? Are they double-spaced, left aligned, and in the same font as the paper?
• Are all figures mentioned in the text?
• Has written permission for print and electronic reuse been obtained? Is proper credit given in the figure caption?
• Have all substantive modifications to photographic images been disclosed?
• Are the figures being submitted in a file format acceptable to the publisher?
• Have the files been produced at a sufficiently high resolution to allow for accurate reproduction?

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Figures and tables

Figures and tables (display items) are often the quickest way to  communicate large amounts of complex information that would be complicated to explain in text.

Many readers will only look at your display items without reading the main text of your manuscript. Therefore, ensure your display items can stand alone from the text and communicate clearly your most significant results.

Display items are also important for  attracting readers  to your work. Well designed and attractive display items will hold the interest of readers, compel them to take time to understand a figure and can even entice them to read your full manuscript.

Finally, high-quality display items give your work a  professional appearance . Readers will assume that a professional-looking manuscript contains good quality science. Thus readers may be more likely to trust your results and your interpretation of those results.

When deciding which of your results to present as display items consider the following questions:

• Are there any data that readers might rather see as a display item rather than text?
• Do your figures supplement the text and not just repeat what you have already stated?
• Have you put data into a table that could easily be explained in the text such as simple statistics or p values?

Tables are a concise and effective way to present large amounts of data. You should design them carefully so that you clearly communicate your results to busy researchers.

The following is an example of a well-designed table:

• Clear and concise legend/caption
• Data divided into categories for clarity
• Sufficient spacing between columns and rows
• Units are provided
• Font type and size are legible

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Your Guide to Creating Effective Tables and Figures in Research Papers

Research papers are full of data and other information that needs to be effectively illustrated and organized. Without a clear presentation of a study's data, the information will not reach the intended audience and could easily be misunderstood. Clarity of thought and purpose is essential for any kind of research. Using tables and figures to present findings and other data in a research paper can be effective ways to communicate that information to the chosen audience.

When manuscripts are screened, tables and figures can give reviewers and publication editors a quick overview of the findings and key information. After the research paper is published or accepted as a final dissertation, tables and figures will offer the same opportunity for other interested readers. While some readers may not read the entire paper, the tables and figures have the chance to still get the most important parts of your research across to those readers.

However, tables and figures are only valuable within a research paper if they are succinct and informative. Just about any audience—from scientists to the general public—should be able to identify key pieces of information in well-placed and well-organized tables. Figures can help to illustrate ideas and data visually. It is important to remember that tables and figures should not simply be repetitions of data presented in the text. They are not a vehicle for superfluous or repetitious information. Stay focused, stay organized, and you will be able to use tables and figures effectively in your research papers. The following key rules for using tables and figures in research papers will help you do just that.

Check style guides and journal requirements

The first step in deciding how you want to use tables and figures in your research paper is to review the requirements outlined by your chosen style guide or the submission requirements for the journal or publication you will be submitting to. For example, JMIR Publications states that for readability purposes, we encourage authors to include no more than 5 tables and no more than 8 figures per article. They continue to outline that tables should not go beyond the 1-inch margin of a portrait-orientation 8.5"x11" page using 12pt font or they may not be able to be included in your main manuscript because of our PDF sizing.

Consider the reviewers that will be examining your research paper for consistency, clarity, and applicability to a specific publication. If your chosen publication usually has shorter articles with supplemental information provided elsewhere, then you will want to keep the number of tables and figures to a minimum.

According to the Purdue Online Writing Lab (Purdue OWL), the American Psychological Association (APA) states that Data in a table that would require only two or fewer columns and rows should be presented in the text. More complex data is better presented in tabular format. You can avoid unnecessary tables by reviewing the data and deciding if it is simple enough to be included in the text. There is a balance, and the APA guideline above gives a good standard cutoff point for text versus table. Finally, when deciding if you should include a table or a figure, ask yourself is it necessary. Are you including it because you think you should or because you think it will look more professional, or are you including it because it is necessary to articulate the data? Only include tables or figures if they are necessary to articulate the data.

Table formatting

Creating tables is not as difficult as it once was. Most word processing programs have functions that allow you to simply select how many rows and columns you want, and then it builds the structure for you. Whether you create a table in LaTeX , Microsoft Word , Microsoft Excel , or Google Sheets , there are some key features that you will want to include. Tables generally include a legend, title, column titles, and the body of the table.

When deciding what the title of the table should be, think about how you would describe the table's contents in one sentence. There isn't a set length for table titles, and it varies depending on the discipline of the research, but it does need to be specific and clear what the table is presenting. Think of this as a concise topic sentence of the table.

Column titles should be designed in such a way that they simplify the contents of the table. Readers will generally skim the column titles first before getting into the data to prepare their minds for what they are about to see. While the text introducing the table will give a brief overview of what data is being presented, the column titles break that information down into easier-to-understand parts. The Purdue OWL gives a good example of what a table format could look like:

When deciding what your column titles should be, consider the width of the column itself when the data is entered. The heading should be as close to the length of the data as possible. This can be accomplished using standard abbreviations. When using symbols for the data, such as the percentage "%" symbol, place the symbol in the heading, and then you will not use the symbol in each entry, because it is already indicated in the column title.

For the body of the table, consistency is key. Use the same number of decimal places for numbers, keep the alignment the same throughout the table data, and maintain the same unit of measurement throughout each column. When information is changed within the same column, the reader can become confused, and your data may be considered inaccurate.

Figures in research papers

Figures can be of many different graphical types, including bar graphs, scatterplots, maps, photos, and more. Compared to tables, figures have a lot more variation and personalization. Depending on the discipline, figures take different forms. Sometimes a photograph is the best choice if you're illustrating spatial relationships or data hiding techniques in images. Sometimes a map is best to illustrate locations that have specific characteristics in an economic study. Carefully consider your reader's perspective and what detail you want them to see.

As with tables, your figures should be numbered sequentially and follow the same guidelines for titles and labels. Depending on your chosen style guide, keep the figure or figure placeholder as close to the text introducing it as possible. Similar to the figure title, any captions should be succinct and clear, and they should be placed directly under the figure.

Using the wrong kind of figure is a common mistake that can affect a reader's experience with your research paper. Carefully consider what type of figure will best describe your point. For example, if you are describing levels of decomposition of different kinds of paper at a certain point in time, then a scatter plot would not be the appropriate depiction of that data; a bar graph would allow you to accurately show decomposition levels of each kind of paper at time "t." The Writing Center of the University of North Carolina at Chapel Hill has a good example of a bar graph offering easy-to-understand information:

If you have taken a figure from another source, such as from a presentation available online, then you will need to make sure to always cite the source. If you've modified the figure in any way, then you will need to say that you adapted the figure from that source. Plagiarism can still happen with figures – and even tables – so be sure to include a citation if needed.

Using the tips above, you can take your research data and give your reader or reviewer a clear perspective on your findings. As The Writing Center recommends, Consider the best way to communicate information to your audience, especially if you plan to use data in the form of numbers, words, or images that will help you construct and support your argument. If you can summarize the data in a couple of sentences, then don't try and expand that information into an unnecessary table or figure. Trying to use a table or figure in such cases only lengthens the paper and can make the tables and figures meaningless instead of informative.

Carefully choose your table and figure style so that they will serve as quick and clear references for your reader to see patterns, relationships, and trends you have discovered in your research. For additional assistance with formatting and requirements, be sure to review your publication or style guide's instructions to ensure success in the review and submission process.

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Effective Use of Tables and Figures in Research Papers

Research papers are often based on copious amounts of data that can be summarized and easily read through tables and graphs. When writing a research paper , it is important for data to be presented to the reader in a visually appealing way. The data in figures and tables, however, should not be a repetition of the data found in the text. There are many ways of presenting data in tables and figures, governed by a few simple rules. An APA research paper and MLA research paper both require tables and figures, but the rules around them are different. When writing a research paper, the importance of tables and figures cannot be underestimated. How do you know if you need a table or figure? The rule of thumb is that if you cannot present your data in one or two sentences, then you need a table .

Using Tables

Tables are easily created using programs such as Excel. Tables and figures in scientific papers are wonderful ways of presenting data. Effective data presentation in research papers requires understanding your reader and the elements that comprise a table. Tables have several elements, including the legend, column titles, and body. As with academic writing, it is also just as important to structure tables so that readers can easily understand them. Tables that are disorganized or otherwise confusing will make the reader lose interest in your work.

• Title: Tables should have a clear, descriptive title, which functions as the “topic sentence” of the table. The titles can be lengthy or short, depending on the discipline.
• Column Titles: The goal of these title headings is to simplify the table. The reader’s attention moves from the title to the column title sequentially. A good set of column titles will allow the reader to quickly grasp what the table is about.
• Table Body: This is the main area of the table where numerical or textual data is located. Construct your table so that elements read from up to down, and not across.

Related: Done organizing your research data effectively in tables? Check out this post on tips for citing tables in your manuscript now!

The placement of figures and tables should be at the center of the page. It should be properly referenced and ordered in the number that it appears in the text. In addition, tables should be set apart from the text. Text wrapping should not be used. Sometimes, tables and figures are presented after the references in selected journals.

Using Figures

Figures can take many forms, such as bar graphs, frequency histograms, scatterplots, drawings, maps, etc. When using figures in a research paper, always think of your reader. What is the easiest figure for your reader to understand? How can you present the data in the simplest and most effective way? For instance, a photograph may be the best choice if you want your reader to understand spatial relationships.

• Figure Captions: Figures should be numbered and have descriptive titles or captions. The captions should be succinct enough to understand at the first glance. Captions are placed under the figure and are left justified.
• Image: Choose an image that is simple and easily understandable. Consider the size, resolution, and the image’s overall visual attractiveness.
• Additional Information: Illustrations in manuscripts are numbered separately from tables. Include any information that the reader needs to understand your figure, such as legends.

Common Errors in Research Papers

Effective data presentation in research papers requires understanding the common errors that make data presentation ineffective. These common mistakes include using the wrong type of figure for the data. For instance, using a scatterplot instead of a bar graph for showing levels of hydration is a mistake. Another common mistake is that some authors tend to italicize the table number. Remember, only the table title should be italicized .  Another common mistake is failing to attribute the table. If the table/figure is from another source, simply put “ Note. Adapted from…” underneath the table. This should help avoid any issues with plagiarism.

Using tables and figures in research papers is essential for the paper’s readability. The reader is given a chance to understand data through visual content. When writing a research paper, these elements should be considered as part of good research writing. APA research papers, MLA research papers, and other manuscripts require visual content if the data is too complex or voluminous. The importance of tables and graphs is underscored by the main purpose of writing, and that is to be understood.

Frequently Asked Questions

"Consider the following points when creating figures for research papers: Determine purpose: Clarify the message or information to be conveyed. Choose figure type: Select the appropriate type for data representation. Prepare and organize data: Collect and arrange accurate and relevant data. Select software: Use suitable software for figure creation and editing. Design figure: Focus on clarity, labeling, and visual elements. Create the figure: Plot data or generate the figure using the chosen software. Label and annotate: Clearly identify and explain all elements in the figure. Review and revise: Verify accuracy, coherence, and alignment with the paper. Format and export: Adjust format to meet publication guidelines and export as suitable file."

"To create tables for a research paper, follow these steps: 1) Determine the purpose and information to be conveyed. 2) Plan the layout, including rows, columns, and headings. 3) Use spreadsheet software like Excel to design and format the table. 4) Input accurate data into cells, aligning it logically. 5) Include column and row headers for context. 6) Format the table for readability using consistent styles. 7) Add a descriptive title and caption to summarize and provide context. 8) Number and reference the table in the paper. 9) Review and revise for accuracy and clarity before finalizing."

"Including figures in a research paper enhances clarity and visual appeal. Follow these steps: Determine the need for figures based on data trends or to explain complex processes. Choose the right type of figure, such as graphs, charts, or images, to convey your message effectively. Create or obtain the figure, properly citing the source if needed. Number and caption each figure, providing concise and informative descriptions. Place figures logically in the paper and reference them in the text. Format and label figures clearly for better understanding. Provide detailed figure captions to aid comprehension. Cite the source for non-original figures or images. Review and revise figures for accuracy and consistency."

"Research papers use various types of tables to present data: Descriptive tables: Summarize main data characteristics, often presenting demographic information. Frequency tables: Display distribution of categorical variables, showing counts or percentages in different categories. Cross-tabulation tables: Explore relationships between categorical variables by presenting joint frequencies or percentages. Summary statistics tables: Present key statistics (mean, standard deviation, etc.) for numerical variables. Comparative tables: Compare different groups or conditions, displaying key statistics side by side. Correlation or regression tables: Display results of statistical analyses, such as coefficients and p-values. Longitudinal or time-series tables: Show data collected over multiple time points with columns for periods and rows for variables/subjects. Data matrix tables: Present raw data or matrices, common in experimental psychology or biology. Label tables clearly, include titles, and use footnotes or captions for explanations."

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Presenting Data and Sources Accurately and Effectively

Table of Contents (Guide To Publication)

Part ii: preparing, presenting and polishing your work – chapter 5, 5. presenting data and sources accurately and effectively.

Journal guidelines vary greatly when it comes to the advice they provide about presenting data and referring to sources. In some cases separate sections containing detailed instructions about exactly how to lay out tables and figures and how to format citations and references will be provided, while in others authors will simply be advised to format tables and figures in ‘an appropriate’ manner and will be lucky to find two or three reference examples to follow. Tables and figures do seem to receive fairly good coverage in the guidelines of most scholarly journals, however, and generally you will be able to find some indication of the referencing style required. So read anything and everything you can find in the guidelines about these elements of your paper, pay careful attention to any models provided (both appropriate and inappropriate), consult any manuals or other style guides mentioned and take a close look at papers already published by the journal to see how references, tables and figures were successfully formatted. What you learn can be both followed and used to inspire your own designs when constructing your references, tables and figures.

5.1 Tables, Figures and Other Research Data: Guidelines and Good Practice

Although the advice I share in this section should not be taken as a substitute for journal guidelines when it comes to the layout of tables and figures, it stems from a familiarity with the guidelines of many journals and the experience of encountering many tables and figures that present unfamiliar data. As with every other aspect of your paper, clarity, accuracy and precision are essential, and in the case of tables and figures, there’s little space for explanation, so data must for the most part stand on their own, with only the format you shape around them to lend structure and meaning. This means that the format of your tables and figures needs to be thought out very carefully: there needs to be enough space both to present and to separate all the information your tables and figures contain in ways that facilitate your readers’ understanding. Poorly laid out tables and figures can instead obscure that understanding, so it’s important to analyse your tables and figures as a reader would, seeking the information you’re providing, and then edit and reshape until your tables and figures achieve just what they should. Some journals will insist that tables and figures only be used if they include or illustrate information not presented elsewhere in the paper, and frown on those that repeat data in any way. So the first consideration should be whether you need tables and figures to share your research and results effectively and, if so, what exactly those tables and figures should contain. Illustrating devices or conditions discussed in a paper, providing graphs and lists of data that cannot be accommodated in detail in an article and highlighting the most significant aspects of the results of a study are a few of many reasons to provide tables and figures for your readers,

Once you’ve decided that your paper does require tables and/or figures, some basic practices and concerns found in the guidelines of many journals should be considered. For tables, for instance, ask yourself if you will you require lines or rules to separate the material – some journals ask that vertical lines be avoided, others that rules of all kinds be avoided, and a table may take more space on the page if you need to construct it without lines. For figures, there is the matter of using colour or not: some journals only print tables in monochrome (black and white) and include figures in colour solely online, while others will be happy to print your figures in colour, but they may charge a significant amount for it, so you’ll need to decide whether printing the figures in colour is worth the cost. For both tables and figures, consider the overall size of each item in terms of the printed page of the journal, and if your tables or figures will need to be reduced to such a degree that they may no longer be clear or legible, you may have to present the information in a different format or divide the information you’ve compiled in one table or figure into two or three tables or figures. Online publication is often the best route for large tables and figures, and colour rarely proves a problem with online publication.

Remember as you’re constructing your tables and figures that as a general rule each table and figure should be able to stand alone, whether it’s printed amidst the text of your paper or published separately online. For this reason, all abbreviations beyond the standard ones for common measures (cm, Hz, mph, N, SD, etc.) will need to be defined either in the table or figure itself or in close association with it, and this is the case even if you’ve already defined the abbreviations in your paper and in any preceding tables or figures. You can choose to write each term out in full within the body of the table or figure, or introduce and define the abbreviations in the heading or title of a table or in the caption or legend of a figure, or you can define any abbreviations used in a note at the bottom of the table or figure. This last approach is used for tables more often than for figures, and the abbreviations within a table are usually connected to the definitions in the note via superscript lowercase letters (but not always, so do check the journal guidelines). If you’re in any doubt about whether an abbreviation should be defined for your readers, it’s best to define it: such attention is a sign of conscientious documentation, and if the journal deems the definition unnecessary, it can always be removed.

Be sure that the terms you use in your tables and figures match those you use in the paper itself precisely, and that the abbreviations take the same forms in both the paper and the tables and figures. In fact, it’s essential to ensure that all the data presented in tables and figures are entirely consistent with data presented in the paper (and the abstract as well). This is to say that the format in which you present similar data in both places should be identical, and any overlapping data should be exactly the same in content as well as format in both places. Remember that data stand alone in a table or figure, so they need to be perfect and should be checked more than once by more than one pair of knowledgeable eyes. Even a simple error can not only render the information incorrect, it can also alter the overall appearance of the table or figure, and since an effective visual representation of information is precisely the goal of tables and figures, this can be disastrous. All numbers in a table or figure can be written as numerals and should be accurately formatted in keeping with English convention and/or journal guidelines (on the use of numbers in academic or scientific prose, see Section 4.4.1 above).

Journal guidelines should also be consulted to determine exactly how to place and submit your tables and figures in relation to your paper. Variations are myriad: when submitting to some journals you can simply place your tables and figures where you’d have them located in the published version; others will want all tables and figures added at the end of the document and only placement notes – e.g., ‘Insert Table 1 here’ and ‘Figure 3 about here’ – within the body of the paper. ‘Added at the end of the document’ can mean either before or after the reference list, and for some journals tables should precede figures, whereas for others it’s just the opposite. Sometimes guidelines will ask that tables be embedded in or tacked onto the end of the paper, but the figures submitted in separate files, with only the figure legends included in the paper, usually at the end. The point is to note and comply with whatever is required: it’s disappointing to discover that guidelines won’t let you use tables and figures quite as you’d hoped, but better that than writing the paper with the tables and figures you want only to have it rejected because of them or (in the best scenario) have to completely rewrite your paper with different, fewer or no tables at all. If a set number or style or size of tables and figures is absolutely central to your paper, then be sure to choose a journal that allows it.

However many or few tables and figures you use, be sure to label each one accurately and to refer to each of them in the body of your paper as you report and discuss your results. Virtually all journal guidelines specify this (and others expect it), and it’s also a simple courtesy to your reader that facilitates that reader’s understanding of your paper and your tables and figures in relation to it. Unnumbered or misnumbered figures and tables to which the reader is not accurately and precisely referred at an appropriate point in the text defeat their own purpose and negate some of the hard work that went into making them by leaving it to the reader to sort out the relationship between your text and your tables and figures. Tables and figures should also be referred to in numerical order, which means that they should be numbered according to the order in which they are mentioned in the text regardless of where they are actually placed in relation to the text. For clarity, they should also be referred to by number whenever mentioned, with the usual format being ‘Figure 1’ or ‘Table 2,’ unless, of course, the journal guidelines specify a different format (such as ‘Fig.1’), and whatever format used to refer to a table or figure should match that used in the heading or caption to label the table or figure itself. In the heading/caption for a table or figure, a full stop usually follows the number (Table 1. Demographic characteristics of study participants) unless there are instructions in the guidelines to the contrary (calling for a colon, for instance, after the number instead of a full stop). The title or heading of a table is generally placed above the table, whereas figure captions or legends often appear beneath figures, but guidelines (as well as style manuals) differ on this as well, so again, reading and following the guidelines of the specific journal is essential to success (see also Section 1.2 above).

Finally, if you are using in your figures any images for which the copyright belongs to someone other than yourself, you’ll need to acknowledge the source(s), usually in the relevant figure captions, and you’ll also need to obtain permissions to reproduce such images. Although all permissions need not be obtained until your paper is accepted for publication in a journal, it’s a good idea to indicate when you submit your paper which figures will require permissions and from which individuals and institutions those permissions will need to be requested, as well as noting any permissions that you’ve already obtained. Planning ahead when it comes to permissions can prevent delays and help speed up the publication process, but remember, too, that permissions to reproduce images from other publications can be costly and the expense is usually met by the author, so it’s a good idea to consider carefully whether reproducing images and other material that require permissions is really necessary and worth the cost.

PRS Tip : So much attention is paid to numerical data in tables and images in figures that the words appearing in tables and figures sometimes suffer neglect. If the words used in tables and figures do not effectively clarify and categorise the information presented, the reader’s understanding suffers as well. So when using words in a table or figure, it’s good to keep these basic practices in mind:

• Use standard abbreviations for measures and define all abbreviations beyond those for common measures.
• Use terms and abbreviations that match exactly those used for the same concepts, categories and measures in the paper and in its other tables and figures.
• Make sure that all words are visible and legible, and not obscured or crowded by other elements of the table or figure.
• Do not allow a word to be split inappropriately onto two separate lines – use a wider column instead.
• Use capitalisation consistently throughout the tables and figures in a paper.
• If the table or figure was originally prepared in another language, translate all words into accurate English – if you’re writing for an English-speaking audience, all aspects of your paper, including your tables and figures, should be entirely legible to that audience.

This article is part of a book called Guide to Academic and Scientific Publication: How To Get Your Writing Published in Scholarly Journals . It provides practical advice on planning, preparing and submitting articles for publication in scholarly journals.

Whether you are looking for information on designing an academic or scientific article, constructing a scholarly argument, targeting the right journal, following journal guidelines with precision, providing accurate and complete references, writing correct and elegant scholarly English, communicating with journal editors or revising your paper in light of that communication, you will find guidance, tips and examples in this manual.

This book is focusing on sound scholarly principles and practices as well as the expectations and requirements of academic and scientific journals, this guide is suitable for use in a wide variety of disciplines, including Economics, Engineering, the Humanities, Law, Management, Mathematics, Medicine and the Social, Physical and Biological Sciences .

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• The Dos and Don’ts of Using Tables and Figures in Your Writing

by acburton | Apr 29, 2024 | Resources for Students , Writing Resources

What do you do when words just aren’t enough?

For subjects outside of the Humanities, including STEM disciplines such as mathematics, physical sciences, and engineering, using tables and figures throughout your writing can effectively break-up longer pieces of text by presenting useful data and statistics. Within the Humanities, the incorporation of multimodal elements is championed in UCI courses (Humanities Core!), and can aid in your construction or support of an argument. But what are some things to keep in mind when including these components alongside your written work?

When incorporating tables and figures into writing, you’ll want to be mindful of the:

Who Needs a Table or Figure?

What is the best way to convey the information you have to a reader? What discipline are you working in? How best can you visualize the data you would like to share?

Starting with these questions (or others like it) is a great place to begin when thinking about whether or not to include tables and figures in your writing. While incorporating the following into research or lab reports for a STEM related course may seem like a no-brainer, you’ll still want to examine what kind of data you’ll want to share with your audience or reader and what is the best way (table or figure) to present it. With your audience in mind, think about their expectations as well as your ability to present your data in the most effective, concise, and efficient manner possible.

Synthesize versus Visualize

The incorporation of tables into your writing often serves as one method for synthesizing information, including existing literature, or to explain variables or present the wording of a specific kind of data (e.g., the wording of survey questions) (UNC). In contrast, figures (images, charts, graphs (pie charts, line graphs, etc.* [1] )) are the visual representation of results (UNC). They can be used to provide a visual component or impact and can effectively communicate primary findings such as the relationship (patterns or trends), between two variables (UNC).

Think of figures like you would paragraphs. If you have several important things to say, consider making more than one table or figure, or incorporating other visual elements, one for each important idea that you would like to share. No matter what, strive for clarity ! Don’t put too much information on your tables or figures, making them crowded or difficult to follow. Likewise, do use consistent elements (such as a uniform font) in your tables or figures so as to not distract your reader or audience.

Let’s go through a few other Dos and Don’ts for incorporating tables and figures into your writing!

use a table or figure in your writing as a method of making your data more concise and presentable.

use tables and figures to enhance or supplement the text. They should be self-explanatory.

• be sure that your tables and figures reflect your data accurately.

Don’ts

• incorporate a table or figure just because you want to reach the page minimum for an assignment.
• use a table or figure solely for aesthetic purposes. This may backfire, as it could demonstrate to your reader that you do not have a solid grasp on the requirements or expectations of the assignment or discipline.
• repeat data already shown on one table or figure.

Remember that this is not an exhaustive list. Strive for clarity whenever you decide to incorporate tables or figures into your writing!

How Tables and Figures Interact with Text

Although tables and figures must be able to stand alone, without additional information provided in the text, it is recommended that you reference your tables and figures within the text, reinforcing your decision to incorporate them into your writing.

To refer to tables and figures from within the text, the University of North Carolina at Chapel Hill suggests beginning your sentences with:

• Clauses beginning with “as”: “As shown in Table 1, …”
• A Passive voice: “Results are shown in Table 1.”
• An Active voice (if appropriate for your discipline): “Table 1 shows that …”
• A Parentheses: “Each sample tested positive for three nutrients (Table 1).”

Another way that tables and figures interact with text is in the captions. Captions should be concise, descriptive, and comprehensive. They should describe what is being shown, draw attention to important features, and, sometimes, may also include interpretations of the data or results (UNC). Figures are typically read from left to right, top to bottom, but for additional formatting information, reference the citation style guide used for your specific assignment. We recommend using Purdue Owl as a resource for additional clarification.

Check out these Dos and Don’ts  surrounding the interaction between tables, figures, and texts!

• clarify any abbreviations you use within the text or in your captions.

consider incorporating your data into the text instead of using a table or figure if there is simple or less data to show.

• repeat data or information already summarized in the text.

Tables, and Figures, and Blog Posts, Oh My! Visit the Writing Center for additional assistance on using tables and figures in your writing!

https://writingcenter.unc.edu/tips-and-tools/figures-and-charts/#:~:text=Think%20of%20graphs%20like%20you,way%20that%20is%20visually%20clear .

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10394528/

https://core.humanities.uci.edu/index.php/spring/multimodal-presentation-tools/

[1] This is not an exhaustive list; don’t forget X, Y scatter plots or XY line graphs are also great examples!

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How to Use Tables and Figures effectively in Research Papers

• 3 minute read
• 43.3K views

Table of Contents

Data is the most important component of any research. It needs to be presented effectively in a paper to ensure that readers understand the key message in the paper. Figures and tables act as concise tools for clear presentation . Tables display information arranged in rows and columns in a grid-like format, while figures convey information visually, and take the form of a graph, diagram, chart, or image. Be it to compare the rise and fall of GDPs among countries over the years or to understand how COVID-19 has impacted incomes all over the world, tables and figures are imperative to convey vital findings accurately.

So, what are some of the best practices to follow when creating meaningful and attractive tables and figures? Here are some tips on how best to present tables and figures in a research paper.

Guidelines for including tables and figures meaningfully in a paper:

• Self-explanatory display items: Sometimes, readers, reviewers and journal editors directly go to the tables and figures before reading the entire text. So, the tables need to be well organized and self-explanatory.
• Avoidance of repetition: Tables and figures add clarity to the research. They complement the research text and draw attention to key points. They can be used to highlight the main points of the paper, but values should not be repeated as it defeats the very purpose of these elements.
• Consistency: There should be consistency in the values and figures in the tables and figures and the main text of the research paper.
• Informative titles: Titles should be concise and describe the purpose and content of the table. It should draw the reader’s attention towards the key findings of the research. Column heads, axis labels, figure labels, etc., should also be appropriately labelled.
• Adherence to journal guidelines: It is important to follow the instructions given in the target journal regarding the preparation and presentation of figures and tables, style of numbering, titles, image resolution, file formats, etc.

Now that we know how to go about including tables and figures in the manuscript, let’s take a look at what makes tables and figures stand out and create impact.

How to present data in a table?

For effective and concise presentation of data in a table, make sure to:

• Combine repetitive tables: If the tables have similar content, they should be organized into one.
• Divide the data: If there are large amounts of information, the data should be divided into categories for more clarity and better presentation. It is necessary to clearly demarcate the categories into well-structured columns and sub-columns.
• Keep only relevant data: The tables should not look cluttered. Ensure enough spacing.

Example of table presentation in a research paper

For comprehensible and engaging presentation of figures:

• Ensure clarity: All the parts of the figure should be clear. Ensure the use of a standard font, legible labels, and sharp images.
• Use appropriate legends: They make figures effective and draw attention towards the key message.
• Make it precise: There should be correct use of scale bars in images and maps, appropriate units wherever required, and adequate labels and legends.

It is important to get tables and figures correct and precise for your research paper to convey your findings accurately and clearly. If you are confused about how to suitably present your data through tables and figures, do not worry. Elsevier Author Services are well-equipped to guide you through every step to ensure that your manuscript is of top-notch quality.

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How to make a scientific table | Step-by-step and Formatting

It’s time to learn how to make a scientific table to increase the readability and attractiveness of your research paper.

When writing a research paper, there is frequently a massive quantity of data that must be incorporated to meet the research’s purpose. Instead of stuffing your research paper with all this information, you can employ visual assets to make it simpler to read and use to your advantage to make it more appealing to readers.

In this Mind The Graph article, you will learn how to make a scientific table properly, to attract readers and improve understandability.

What is a scientific table and what are its purposes?

Tables are typically used to organize data that is too extensive or nuanced to properly convey in the text, allowing the reader to quickly see and comprehend the findings. Tables can be used to summarize information, explain variables, or organize and present surveys. They can be used to highlight trends or patterns in data and to make research more readable by separating numerical data from text. Tables, although full, should not be overly convoluted.

Tables can only display numerical values and text in columns and rows. Any other type of illustration, such as a chart, graph, photograph, drawing, and so on is called a figure.

If you’re not sure whether to use tables or figures in your research, see How to Include Figures in a Research Paper to find out.

Table formatting

This section teaches you all you need to know on how to make a scientific table to include in your research paper. The proper table format is extremely basic and straightforward to accomplish, here’s a simple guideline to help you:

• Number: If you have more than one table, number them sequentially (Table 1, Table 2…).
• Referencing: Each table must be referred to in the text with a capital T: “as seen in Table 1”.
• Title: Make sure the title corresponds to the topic of the table. Tables should have a precise, informative title that serves as an explanation for the table. Titles can be short or long depending on their subject.
• Column headings: Headings must be helpful and clear when representing the type of data provided. The reader’s attention is drawn progressively from the headline to the column title. A solid collection of column headings will help the reader understand what the table is about immediately.
• Table body: This is the major section of the table that contains numerical or textual data. Make your table such that the elements read from top to bottom, not across.
• Needed information: Make sure to include units, error values and number of samples, as well as explain whatever abbreviation or symbol is used in tables. 
• Lines: Limit the use of lines, only use what’s necessary. 

Steps to make an effective scientific table

Now that you understand the fundamentals of how to make a scientific table , consider the following ideas and best practices for creating the most effective tables for your research work:

• If your study includes both a table and a graph, avoid including the same information in both.
• Do not duplicate information from a table in a text.
• Make your table aesthetically appealing and easy to read by leaving enough space between columns and rows and using a basic yet effective structure.
• If your table has a lot of information, consider categorizing it and dividing it into columns.
• Consider merging tables with repeated information or deleting those that may not be essential.
• Use footnotes to highlight important information for any of the cells. Use an alphabetical footnote marker if your table contains numerical data. 
• Cite the reference if the table you’re displaying contains data from prior research to avoid plagiarism.

Make scientifically accurate infographics in minutes

Aside from adding tables to make your research paper more precise and appealing, consider using infographics, Mind the Graph is a simple tool for creating excellent scientific infographics that may help you solidify and improve the authority of your research.

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What is a table?

A table is a set of data (descriptive or numerical) formatted in rows and columns. They are used to display information that is too complex or granular to read in the main text or a list. When deciding how to best present data in your work, keep in mind that:

• A table should support, not repeat, information given in the text.
• It should stand independently so that readers can retrieve information without referring to the main text.  
• Tables that span several pages are difficult to follow – keep your table size compact.
• Distinguish between figures, tables, and boxes as this has implications for digital production. Table 5 shows some differences between tables, figures, and box elements:

Table 5 : Differences between figures, boxes, and tables.

Key steps for creating tables

There are a few important tasks to complete when adding tables to your manuscript.

If you (or a co-author) are not the original creator of the table, you must get permission to use it from the copyright holder. For instructions, see ‘ Permissions Guidelines ’ and ‘ Crediting copyrighted material ’.

Within the text, you must refer to the table with a call-out and indicate its placement in your instructions. Table 6 provides instructions and examples to guide you.

Table 6 : Instructions for creating tables.

How to draw a table in MS Word

• Use the MS Word table tool (Insert > Table).
• Hide the ruled markings, which are required only horizontally before and after titles and at the foot of the table, before footnotes.
• Left align all text.
• Right align all numbers.
• Provide a clear heading for each column, including units of measure.
• Extra comments or references associated with table data should be added as footnotes below the table. Notes should be in two lists, as applicable: general notes that apply to the whole table, and notes corresponding to specific elements of the table (using letters not numbers, see the example below). Notes should not be part of the chapter footnote sequence.
• Adhere to OUP house style , matching terminology, formatting, and phrasing to the text.
• All cells must have entries, even if only ‘n.a.’ (‘not applicable/not available’) or a dash for ‘no data’.

Figure 22 : Example of a correctly formatted table.

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Literature Review Basics

• What is a Literature Review?
• Synthesizing Research
• Using Research & Synthesis Tables
• Additional Resources

About the Research and Synthesis Tables

Research Tables and Synthesis Tables are useful tools for organizing and analyzing your research as you assemble your literature review. They represent two different parts of the review process: assembling relevant information and synthesizing it. Use a Research table to compile the main info you need about the items you find in your research -- it's a great thing to have on hand as you take notes on what you read! Then, once you've assembled your research, use the Synthesis table to start charting the similarities/differences and major themes among your collected items.

We've included an Excel file with templates for you to use below; the examples pictured on this page are snapshots from that file.

• Research and Synthesis Table Templates This Excel workbook includes simple templates for creating research tables and synthesis tables. Feel free to download and use!

Using the Research Table

This is an example of a  research table,  in which you provide a basic description of the most important features of the studies, articles, and other items you discover in your research. The table identifies each item according to its author/date of publication, its purpose or thesis, what type of work it is (systematic review, clinical trial, etc.), the level of evidence it represents (which tells you a lot about its impact on the field of study), and its major findings. Your job, when you assemble this information, is to develop a snapshot of what the research shows about the topic of your research question and assess its value (both for the purpose of your work and for general knowledge in the field).

Think of your work on the research table as the foundational step for your analysis of the literature, in which you assemble the information you'll be analyzing and lay the groundwork for thinking about what it means and how it can be used.

Using the Synthesis Table

This is an example of a  synthesis table  or  synthesis matrix , in which you organize and analyze your research by listing each source and indicating whether a given finding or result occurred in a particular study or article ( each row lists an individual source, and each finding has its own column, in which X = yes, blank = no). You can also add or alter the columns to look for shared study populations, sort by level of evidence or source type, etc. The key here is to use the table to provide a simple representation of what the research has found (or not found, as the case may be). Think of a synthesis table as a tool for making comparisons, identifying trends, and locating gaps in the literature.

How do I know which findings to use, or how many to include?  Your research question tells you which findings are of interest in your research, so work from your research question to decide what needs to go in each Finding header, and how many findings are necessary. The number is up to you; again, you can alter this table by adding or deleting columns to match what you're actually looking for in your analysis. You should also, of course, be guided by what's actually present in the material your research turns up!

• << Previous: Synthesizing Research
• Next: Additional Resources >>
• Last Updated: Sep 26, 2023 12:06 PM
• URL: https://usi.libguides.com/literature-review-basics

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How to Use Tables & Graphs in a Research Paper

It might not seem very relevant to the story and outcome of your study, but how you visually present your experimental or statistical results can play an important role during the review and publication process of your article. A presentation that is in line with the overall logical flow of your story helps you guide the reader effectively from your introduction to your conclusion. 

If your results (and the way you organize and present them) don’t follow the story you outlined in the beginning, then you might confuse the reader and they might end up doubting the validity of your research, which can increase the chance of your manuscript being rejected at an early stage. This article illustrates the options you have when organizing and writing your results and will help you make the best choice for presenting your study data in a research paper.

Why does data visualization matter?

Your data and the results of your analysis are the core of your study. Of course, you need to put your findings and what you think your findings mean into words in the text of your article. But you also need to present the same information visually, in the results section of your manuscript, so that the reader can follow and verify that they agree with your observations and conclusions. 

The way you visualize your data can either help the reader to comprehend quickly and identify the patterns you describe and the predictions you make, or it can leave them wondering what you are trying to say or whether your claims are supported by evidence. Different types of data therefore need to be presented in different ways, and whatever way you choose needs to be in line with your story. 

Another thing to keep in mind is that many journals have specific rules or limitations (e.g., how many tables and graphs you are allowed to include, what kind of data needs to go on what kind of graph) and specific instructions on how to generate and format data tables and graphs (e.g., maximum number of subpanels, length and detail level of tables). In the following, we will go into the main points that you need to consider when organizing your data and writing your result section .

Table of Contents:

Types of data , when to use data tables .

• When to Use Data Graphs 

Common Types of Graphs in Research Papers 

Journal guidelines: what to consider before submission.

Depending on the aim of your research and the methods and procedures you use, your data can be quantitative or qualitative. Quantitative data, whether objective (e.g., size measurements) or subjective (e.g., rating one’s own happiness on a scale), is what is usually collected in experimental research. Quantitative data are expressed in numbers and analyzed with the most common statistical methods. Qualitative data, on the other hand, can consist of case studies or historical documents, or it can be collected through surveys and interviews. Qualitative data are expressed in words and needs to be categorized and interpreted to yield meaningful outcomes. 

Quantitative data example: Height differences between two groups of participants Qualitative data example: Subjective feedback on the food quality in the work cafeteria

Depending on what kind of data you have collected and what story you want to tell with it, you have to find the best way of organizing and visualizing your results.

When you want to show the reader in detail how your independent and dependent variables interact, then a table (with data arranged in columns and rows) is your best choice. In a table, readers can look up exact values, compare those values between pairs or groups of related measurements (e.g., growth rates or outcomes of a medical procedure over several years), look at ranges and intervals, and select specific factors to search for patterns. 

Tables are not restrained to a specific type of data or measurement. Since tables really need to be read, they activate the verbal system. This requires focus and some time (depending on how much data you are presenting), but it gives the reader the freedom to explore the data according to their own interest. Depending on your audience, this might be exactly what your readers want. If you explain and discuss all the variables that your table lists in detail in your manuscript text, then you definitely need to give the reader the chance to look at the details for themselves and follow your arguments. If your analysis only consists of simple t-tests to assess differences between two groups, you can report these results in the text (in this case: mean, standard deviation, t-statistic, and p-value), and do not necessarily need to include a table that simply states the same numbers again. If you did extensive analyses but focus on only part of that data (and clearly explain why, so that the reader does not think you forgot to talk about the rest), then a graph that illustrates and emphasizes the specific result or relationship that you consider the main point of your story might be a better choice.

When to Use Data Graphs

Graphs are a visual display of information and show the overall shape of your results rather than the details. If used correctly, a visual representation helps your (or your reader’s) brain to quickly understand large amounts of data and spot patterns, trends, and exceptions or outliers. Graphs also make it easier to illustrate relationships between entire data sets. This is why, when you analyze your results, you usually don’t just look at the numbers and the statistical values of your tests, but also at histograms, box plots, and distribution plots, to quickly get an overview of what is going on in your data.

Line graphs

When you want to illustrate a change over a continuous range or time, a line graph is your best choice. Changes in different groups or samples over the same range or time can be shown by lines of different colors or with different symbols.

Example: Let’s collapse across the different food types and look at the growth of our four fish species over time.

You should use a bar graph when your data is not continuous but divided into categories that are not necessarily connected, such as different samples, methods, or setups. In our example, the different fish types or the different types of food are such non-continuous categories.

Example: Let’s collapse across the food types again and also across time, and only compare the overall weight increase of our four fish types at the end of the feeding period.

Scatter plots

Scatter plots can be used to illustrate the relationship between two variables — but note that both have to be continuous. The following example displays “fish length” as an additional variable–none of the variables in our table above (fish type, fish food, time) are continuous, and they can therefore not be used for this kind of graph. 

As you see, these example graphs all contain less data than the table above, but they lead the reader to exactly the key point of your results or the finding you want to emphasize. If you let your readers search for these observations in a big table full of details that are not necessarily relevant to the claims you want to make, you can create unnecessary confusion. Most journals allow you to provide bigger datasets as supplementary information, and some even require you to upload all your raw data at submission. When you write up your manuscript, however, matching the data presentation to the storyline is more important than throwing everything you have at the reader. 

Don’t forget that every graph needs to have clear x and y axis labels , a title that summarizes what is shown above the figure, and a descriptive legend/caption below. Since your caption needs to stand alone and the reader needs to be able to understand it without looking at the text, you need to explain what you measured/tested and spell out all labels and abbreviations you use in any of your graphs once more in the caption (even if you think the reader “should” remember everything by now, make it easy for them and guide them through your results once more). Have a look at this article if you need help on how to write strong and effective figure legends .

Even if you have thought about the data you have, the story you want to tell, and how to guide the reader most effectively through your results, you need to check whether the journal you plan to submit to has specific guidelines and limitations when it comes to tables and graphs. Some journals allow you to submit any tables and graphs initially (as long as tables are editable (for example in Word format, not an image) and graphs of high enough resolution. 

Some others, however, have very specific instructions even at the submission stage, and almost all journals will ask you to follow their formatting guidelines once your manuscript is accepted. The closer your figures are already to those guidelines, the faster your article can be published. This PLOS One Figure Preparation Checklist is a good example of how extensive these instructions can be – don’t wait until the last minute to realize that you have to completely reorganize your results because your target journal does not accept tables above a certain length or graphs with more than 4 panels per figure. 

Some things you should always pay attention to (and look at already published articles in the same journal if you are unsure or if the author instructions seem confusing) are the following:

• How many tables and graphs are you allowed to include?
• What file formats are you allowed to submit?
• Are there specific rules on resolution/dimension/file size?
• Should your figure files be uploaded separately or placed into the text?
• If figures are uploaded separately, do the files have to be named in a specific way?
• Are there rules on what fonts to use or to avoid and how to label subpanels?
• Are you allowed to use color? If not, make sure your data sets are distinguishable.

If you are dealing with digital image data, then it might also be a good idea to familiarize yourself with the difference between “adjusting” for clarity and visibility and image manipulation, which constitutes scientific misconduct .  And to fully prepare your research paper for publication before submitting it, be sure to receive proofreading services , including journal manuscript editing and research paper editing , from Wordvice’s professional academic editors .

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How to Format a Table

In any research paper, it is important to be succinct, and help the reader to find the information that they need quickly, and without wading through reams of superfluous information.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

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• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
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• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

One of the best ways to do this is by including relevant data in a table, presenting your findings in an easily digested format.

For any scientist, knowing how to format a table is an essential skill for writing any research paper . Most word processing programs allow you to create tables easily, and you can import or cut and paste tables from spreadsheet applications very easily.

When to Include a Table

For a data heavy paper, including at least one table is essential, and presents the data clearly and succinctly.

A table should be direct and to the point, without including all of the raw data that belongs in the appendix . It should replace a lot of text and explanation, making the results and discussion part of the paper shorter and more clear, although it must be referred to in the text, rather than left to stand alone.

How Much Information Should a Table Contain?

The simple answer to this is a much as is needs and not more than is necessary!

Practically speaking, this means that a table need not contain all of the raw data from your research or complicated statistical breakdowns. It should include enough, however, that a reader can see any trends apparent in the data, especially those highlighted in the text.

Once you have decided upon the information to include, you can begin to format the table.

In a research paper, a table should span the entire page, although many journals prefer smaller tables sets as floating blocks to the left or the right of the text.

Longer tables can carry on for more than one page, especially in the appendix section of the paper, but you should always repeat the headings at the top of each page, so that the reader does not have to keep flicking backwards and forwards.

If a table is too wide to fit across a page without cramping the text too much, it is perfectly fine to use a landscape format, where the table is presented on its side.

Conversely, if a long table only has 2 or 3 columns, you can double it up on the same page, as long as you use a clear dividing line between the two sections.

Format a Table - The Fine Details

There is always the temptation to use some of the many automatic formats included in word processors, with partial shading and highlighting.

In a research paper, you should keep the format simple, with plain black borders. Anything else just overcomplicates things - it is the information in your table that is important, not how it looks.

Finally, tables should be numbered consecutively, as Table 1, Table 2. They are kept apart from diagrams, graphs and photos, which are all referred to as Figures.

The only other distinction is that you should number tables in the appendix separately, as Table A1, Table A2, to prevent confusion.

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Martyn Shuttleworth (Nov 23, 2009). How to Format a Table. Retrieved May 01, 2024 from Explorable.com: https://explorable.com/how-to-format-a-table

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Mastering Verb Tenses in Literature Reviews

In this blog, we will see what tense you should use in the literature review section of your research paper. We will look at some examples of literature review excerpts from published research papers and understand the tenses used in them.

1. Which Tense to Use?

You may have to use multiple tenses in your literature review depending on what you are saying. The present tense is generally used in statements to introduce the literature review, and the past tense is typically used when you are talking about specific papers. The following table summarizes different types of statements you might typically include in your literature review and the corresponding tenses you should use.

2. Usage Example #1

In the first statement, we make a general statement about the topic by saying that this topic has been studied extensively in the literature, and therefore we have used the present tense. In the second sentence, we say that although there has been a lot of research on this topic, it is still relevant today and therefore we have used the present perfect tense.

✔ Example of simple present & present perfect tense use This is widely reported and extensively explored in the literature. This has been of interest for a considerable period, ranging from the early 90s. _   Broad summary (Simple Present) _   Ongoing situation (Present Perfect)

3. Usage Example #2

In the first statement, we begin the literature review by saying that there are many techniques available in the literature to combat weight gain. This statement is only a general summary of previous research on this topic. So we used the present perfect tense. Then, when we then talk about individual works from the literature, we present their results in the past tense.

✔ Example of present perfect & past tense use A number of solutions have been proposed to deal with the problem of weight gain and obesity. He simplest approach was proposed by Lee et al. (2003) which involves fasting. Wang et al. (2010) proposed an alternative solution of using an intragastric balloon. _   Broad summary (Present Perfect) _   Specific papers from past (Past Tense)

4. Usage Example #3

In the following example, in the first sentence we are talking about a collective finding that is generally accepted in the field, and therefore we have used the present perfect tense. In the second statement, we are talking about a finding from a specific work conducted in the past and have therefore used the past tense.

✔ Example of present perfect & past tense use It has been shown that there is a direct correlation between social media and children’s mental health[1-10]. Recently, Elan et al. [11] showed that these findings also applies to teenagers and older population. _   Accepted findings (Present Perfect) _   Specific paper from past (Past Tense)

5. Usage Example #4

In the example below, we talk about past research papers and that’s why we have used the past tense.

✔ Example of past tense use In the initial work presented by Kim et al. (2004), they showed that there is a direct link between Vitamin C and obesity. Smith et al. (2006) replicated this finding in a much larger study. Recently, it was pointed out by Young et al. (2010) that Vitamin C is also related to many other diseases. _   Specific papers from past (Past Tense)

If you have any questions, please drop a comment below, and we will answer as soon as possible. We also recommend you to refer to our other blogs on  academic writing tools ,   academic writing resources ,  academic writing phrases and research paper examples which are relevant to the topic discussed in this blog. 

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

Published on 29.4.2024 in Vol 26 (2024)

Exploring the Impact of In Basket Metrics on the Adoption of a New Electronic Health Record System Among Specialists in a Tertiary Hospital in Alberta: Descriptive Study

Authors of this article:

Original Paper

• Melita Avdagovska 1 , PhD   ; 
• Craig Kuziemsky 2 , PhD   ; 
• Helia Koosha 1 , MSc   ; 
• Maliheh Hadizadeh 1 , PhD   ; 
• Robert P Pauly 3 , MSc, MD   ; 
• Timothy Graham 4 , MSc, MD, CCFP, CHE   ; 
• Tania Stafinski 1 , PhD   ; 
• David Bigam 5 , MD   ; 
• Narmin Kassam 3 , MD   ; 
• Devidas Menon 1 , PhD  

1 School of Public Health, University of Alberta, Edmonton, AB, Canada

2 Office of Research Services and School of Business, MacEwan University, Edmonton, AB, Canada

3 Medicine Department, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada

4 Department of Emergency Medicine, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada

5 Surgery Department, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada

Corresponding Author:

Melita Avdagovska, PhD

School of Public Health

University of Alberta

11405 87 Avenue

Edmonton Clinic Health Academy

Edmonton, AB, T6G 1C9

Phone: 1 780 908 3334

Email: [email protected]

Background: Health care organizations implement electronic health record (EHR) systems with the expectation of improved patient care and enhanced provider performance. However, while these technologies hold the potential to create improved care and system efficiencies, they can also lead to unintended negative consequences, such as patient safety issues, communication problems, and provider burnout.

Objective: This study aims to document metrics related to the In Basket communication hub ( time in In Basket per day, time in In Basket per appointment, In Basket messages received per day, and turnaround time ) of the EHR system implemented by Alberta Health Services, the province-wide health delivery system called Connect Care (Epic Systems). The objective was to identify how a newly implemented EHR system was used, the timing of its use, and the duration of use specifically related to In Basket activities.

Methods: A descriptive study was conducted. Due to the diversity of specialties, the providers were grouped into medical and surgical based on previous similar studies. The participants were further subgrouped based on their self-reported clinical full-time equivalent (FTE ) measure. This resulted in 3 subgroups for analysis: medical FTE <0.5 , medical FTE >0.5 , and surgical (all of whom reported FTE >0.5 ). The analysis was limited to outpatient clinical interactions and explicitly excluded inpatient activities.

Results: A total of 72 participants from 19 different specialties enrolled in this study. The providers had, on average, 8.31 appointments per day during the reporting periods. The providers received, on average, 21.93 messages per day, and they spent 7.61 minutes on average in the time in In Basket per day metric and 1.84 minutes on average in the time in In Basket per appointment metric. The time for the providers to mark messages as done ( turnaround time ) was on average 11.45 days during the reporting period. Although the surgical group had, on average, approximately twice as many appointments per scheduled day, they spent considerably less connected time (based on almost all time metrics) than the medical group. However, the surgical group took much longer than the medical group to mark messages as done ( turnaround time).

Conclusions: We observed a range of patterns with no consistent direction. There does not seem to be evidence of a “learning curve,” which would have shown a consistent reduction in time spent on the system over time due to familiarity and experience. While this study does not show how the included metrics could be used as predictors of providers’ satisfaction or feelings of burnout, the use trends could be used to start discussions about future Canadian studies needed in this area.

Introduction

Electronic health record (EHR) systems have been implemented with many goals including streamlining information sharing among providers, empowering patients to be active partners in their care, supporting evidence-based individualized care, and monitoring population health. Health care organizations implement EHR systems with the expectation of improved patient care and enhanced provider performance [ 1 , 2 ].

EHR systems are not new in Canada [ 3 ]; however, their implementation has been faced with delays, changes in vendors, and reluctant adoption by users [ 4 ]. Canada continues to see activity in EHR implementation including in British Columbia [ 5 ], Saskatchewan [ 6 ], Ontario [ 7 ], Alberta (Connect Care; Epic Systems) [ 8 ], and Nova Scotia [ 9 ].

While EHR systems hold the potential to improve care delivery, they can also contribute to unintended negative consequences, such as patient safety issues, communication problems, and provider burnout [ 10 - 13 ]. Rather than implementing EHR systems and waiting to identify unintended consequences, we should proactively identify metrics to measure the impact of these EHR systems on the work of health care providers and enable ways to improve the diffusion and the subsequent adoption of EHR systems in Canada [ 9 - 12 ].

Advantages of the EHR System

EHRs are systems designed “to collect patient data in real time to enhance care by providing data at the provider’s fingertips and enabling decision-making where it needs to occur” [ 14 ]. These systems provide functions such as viewing (eg, laboratory or test results); documenting (eg, entering data and notes); ordering (eg, referrals, prescriptions, and tests); web-based messaging (eg, notifying patients of test results); care management (eg, disease-specific tools and allergy alerts); analysis and reporting; and patient-directed engagement capabilities (eg, access to own laboratory values and web-based messaging care providers) [ 15 - 20 ]. EHRs can provide benefits such as easy access to accurate and timely point-of-care data, easy navigation to enhance workflow, automation of mundane tasks, evidence-based management pathways to individualize care, convenient sharing of data across organizations, and population health monitoring [ 13 , 19 , 21 ]. Furthermore, when fully implemented, in some instances, EHRs have resulted in the comprehensive replacement of traditional paper charts [ 13 ].

Burden of EHR System

Although EHR systems are designed to deliver positive outcomes, unintended and technology-specific negative outcomes have also been described related to the workflow, patient-provider interactions (technology seen as impersonal), and the challenges of implementing these technologies within current health care systems [ 10 ]. There is increasing awareness that physician well-being has an important impact on the health system, and concerns exist over increasing rates of burnout [ 11 ], job dissatisfaction, intention to leave practice, and job turnover [ 22 ]. Among the consistently reported drivers of burnout and job dissatisfaction are adverse clinician interactions with EHRs. While EHRs are intended to streamline workflows, they are cited as increasing the inefficiency of clinical work, adding to user frustration [ 23 ].

Studies suggest that in clinical environments that include an integrated EHR system, physicians spend an additional 1.5 hours [ 16 ] of time using the EHR system for every 1 hour of direct patient interaction, with an additional 6 to 30 hours [ 17 , 24 ] per month of cumulative time spent on EHR documentation and inbox management outside routine working hours [ 25 ]. Furthermore, studies have demonstrated a negative impact on providers’ time due to managing test results and communications within In Basket, the EHR system’s communication hub [ 24 , 26 ] for administrative, clerical, and documentation functions and after-hour activities related to accomplishing required tasks [ 23 ]. In Basket is where health care providers receive and manage various tasks; web-based messages; and notifications such as appointment requests, medication refill requests, laboratory or imaging results, consultation requests, and web-based messages from other health care team members [ 27 ]. Many of these tasks, previously performed by administrative staff, have been increasingly offloaded to providers.

In a primary care setting, a health care provider may receive anywhere from a few to several dozen In Basket messages per day [ 28 ]. In specialized settings, such as a hospital or specialized clinic, the volume of In Basket messages can be higher, especially for providers who are involved in complex cases or have a larger patient population [ 29 , 30 ]. Specialists may receive additional types of web-based messages, such as interdepartmental consult requests or referrals from primary care providers. Due to these challenges, the time spent in In Basket activities can vary depending on factors such as the volume of web-based messages, complexity of tasks, and individual work practices [ 26 ]. The number of In Basket messages received per day can vary significantly depending on several factors, such as the size of the health care organization, the specialty or department, and the individual provider’s practice [ 31 ]. Managing In Basket time requires a balance between efficiency, prioritization, and effective communication to ensure timely and appropriate handling of tasks while delivering quality patient care [ 26 ]. Health care organizations have attempted to manage these challenges with one-on-one provider training, optimization and upgrade of processes, increased availability of technical support, added or expanded use of scribes, voice recognition, and improved EHR governance [ 32 , 33 ].

While physician burnout and distress, more broadly, are prevalent issues in Canada [ 34 ], to our knowledge, research on EHR use and physician well-being in a Canadian context has been limited, so the relationship of EHRs to surrogate outcomes of well-being (eg, burnout) in the Canadian context is unknown. This knowledge gap is significant, given the substantial disparities between the Canadian and American health care systems, particularly concerning documentation requirements for billing, insurance, and medicolegal purposes, which EHR systems are designed to streamline [ 32 ]. Notably, while EHR system implementation is underway in Canadian hospitals, most US hospitals have already adopted these EHR systems [ 35 , 36 ].

Canada operates under a federated system where health governance is federal, but health care delivery and associated tasks such as EHR implementation are managed provincially. Moreover, unlike in the United States, where much of the charting is focused on billing, the Canadian health care landscape differs substantially, as billing is not part of the need for implementing EHR systems [ 35 , 36 ]. These contextual distinctions between the 2 health care systems mean that research findings from one setting cannot simply be extrapolated to the other [ 13 , 21 , 23 , 24 , 26 , 37 ].

The extent to which EHRs contribute to physician dissatisfaction in Canada, akin to their presumed impact in the United States, remains uncertain. This study lays the groundwork for addressing this gap in knowledge by studying the use metrics of an EHR system implementation in Alberta. This study provides essential insights that not only pave the way for future investigations into the correlation between clinician well-being and EHRs in Canadian contexts but also inform interventional studies aimed at enhancing the user experience. In addition, our findings contribute to the development of best practices for EHR system implementation and use.

This study documented In Basket metrics of an EHR system implemented by Alberta Health Services (AHS), the province-wide health delivery system in Alberta branded as Connect Care. Understanding and documenting granular use metrics of Connect Care in Alberta is foundational for future studies examining the relationship between clinician wellness and EHRs in a Canadian setting, for example, understanding how the EHR-clinician interface contributes to adverse unintended consequences such as burnout. Through gaining an in-depth understanding of how the EHR system in Alberta captures In Basket metrics, this study was designed as a precursor to forthcoming studies examining the association between provider wellness and EHR systems in Canadian settings and to studies focused on improving the EHR system’s user experiences and developing best practices for the EHR system rollout and subsequent use.

Study Design

This was a descriptive study of a volunteer cohort of multidisciplinary specialists working at the University of Alberta Hospital (UAH) [ 38 ]. As the goal of the study was to measure the trends in In Basket use in the EHR system from the launch of Connect Care by specialists at the UAH over 33 months of use, this study design allowed us to commence the identification of how the EHR was used, when it was used, and for how long it was used related to the In Basket activities. To the best of our knowledge, this study is the first to explore the In Basket use of Connect Care by specialists in Alberta.

Explored In Basket Domain Measures (Metrics)

In Basket metrics refer to performance indicators that assess the efficiency and effectiveness of managing tasks, web-based messages, or alerts within the EHR system. These metrics assess various aspects of workflow management and communication within the EHR environment. In this study, we used the following In Basket metrics to capture the use of the In Basket toolbar by study participants: time spent in the In Basket per day , time spent in the In Basket per appointment , number of In Basket messages received per day , and turnaround Time . The time in In Basket per day metric is defined as the average number of minutes a provider spends in In Basket per day. The time in In Basket per appointment metric is the average number of minutes a provider spends in In Basket per scheduled appointment. The In Basket messages received per day metric is the average number of In Basket messages a provider receives per day. The turnaround time metric is the average number of days a provider takes to mark a message of a specific type as done. Furthermore, In Basket metrics included appointments per day, which is the average number of appointments per day within the reporting period for comparison purposes (workload vs use) between the participating specialists.

Ethics Approval

Ethics approval was received from the University of Alberta Health Research Ethics Board (study ID Pro00119194), and operational approval was received from AHS (OA60778, OA60779, and OA60780).

Connect Care is a comprehensive EHR system that allows users to access, generate, and manage documents, laboratory results, text reports, radiology images, notes, prescriptions, referrals, and web-based messages. Furthermore, Connect Care contains advanced auditing capabilities that record the actions of users when accessing the EHR system.

Study Setting

AHS is Canada’s largest integrated provincial health system and is responsible for delivering health services to >4.3 million people. Health care programs and services are offered at >900 facilities throughout the province (eg, hospitals, clinics, continuing care facilities, cancer centers, mental health facilities, and community health sites) [ 39 ]. The UAH is a quaternary care research and teaching hospital in Edmonton, Alberta. This hospital provides a wide range of inpatient and outpatient diagnostic and treatment services [ 40 ]. Study sites within the UAH were selected based on the length of time that they had been using the EHR system. The departments of medicine and surgery at the UAH were part of the first wave of the AHS Connect Care implementation. The specialists in these departments were considered to have used Connect Care for a time period that would provide sufficient use data required for this study.

Study Sample Recruitment

We decided on the following inclusion criteria for potential study participants: (1) any specialist located at the UAH and (2) ≥7 months of Connect Care use.

We used a purposive sampling method to recruit specialists. The clinical coinvestigators (RPP, DB, and NK) introduced and explained the project at departmental meetings. RPP developed a PowerPoint (Microsoft Corp) presentation, which was adapted by DB and NK to fit the context of their respective departments. During these presentations, the coinvestigators started by describing the potential impact of EHRs on provider well-being, the lack of Canadian use data, the need to understand the user experience, and the opportunity for EHR improvement driven by users. Furthermore, potential participants were informed that their individual results from the study would be shared with them. The clinical leads emailed all attending specialists asking them to complete the consent form (using REDCap [Research Electronic Data Capture]; Vanderbilt University) and provide the required information (eg, department, EHR login ID, clinical workload defined by the self-reported fraction of a full-time equivalent [FTE] measure, and work position) for data access.

Data Source

The raw In Basket data source was from Signal (an analytical platform developed by Epic Systems Corporation) using EHR user action log data (Epic Systems Corporation, unpublished data, April 2023). The user action log measures the time that the user interacted with the EHR system. The metrics captured in Signal are defined, and quantifiable measurements are used in reports to summarize information about processes or outcomes (Epic Systems Corporation, unpublished data, September 2020). Information about time spent in particular ambulatory (outpatient) In Basket activities (user action logs) was obtained for each participant from their first login to the EHR system. The analysis was limited to outpatient clinical interactions and explicitly excluded inpatient activities.

Once a specialist agreed to participate in the study, their name, login ID, and study ID were stored in a zipped and encrypted file and sent to the AHS Connect Care and Epic data team through REDCap to retrieve the required event logs data. REDCap is a secure web-based platform hosted by the Women and Children’s Health Research Institute in collaboration with the Northern Alberta Clinical Trials and Research Centre at the University of Alberta. Once the Epic data team reviewed the requested information, data were pulled and transferred to the AHS Connect Care team. The anonymized data were zipped and encrypted before being transferred to the principal investigator for analysis.

Data Description

Participants.

A total of 72 participants from 19 different specialties enrolled in this study. Of the 72 providers, 1 (1%) provider was excluded due to an absence of In Basket outpatient ambulatory Signal data. Due to the diversity of specialties, the providers were grouped into a medical group and a surgical group based on previous similar studies and the fact that these categories have similar EHR workflows [ 41 , 42 ].

The participants were further subgrouped based on their self-reported clinical FTE measure. Clinical FTE is a measure used in health care to quantify the work hours of health care providers or clinical staff in relation to a full-time position. This resulted in 3 subgroups for analysis: medical FTE <0.5 , medical FTE >0.5 , and surgical (all of whom reported FTE >0.5) groups.

In this study, providers in each group are independent of each other (ie, each provider contributes to the weighted means of only 1 group). However, for each In Basket metric, various subsets of providers in the group (ie, medical FTE <0.5, medical FTE >0.5, or surgical group) contribute to the weighted mean of various reporting periods.

Missing Values

Once the EHR data for each of the 72 providers was received, we identified missing values. As this study is one of the first to explore the provider’s use of Connect Care in the Alberta context, we wanted to gain an in-depth understanding of the missing In Basket outpatient ambulatory provider-related Signal data.

On the basis of discussions with the Epic team, the study team identified 3 reasons for missing values in the data. The first reason was that a participant must be “registered” with Connect Care (AHS), be active, and must have logged in to the EHR system and seen at least 1 patient in the reporting period [ 38 ]. Second, for the time in In Basket per appointment metric, there was an additional inclusion criterion where the provider needed at least 5 appointments scheduled per week within the reporting period for Signal to capture user interactions in the EHR system [ 2 ]. We identified this as an issue as many part-time specialists might have ≤4 appointments per week; for example, if they were on ward duties, they would be managing only inpatients during that time. Although they interacted with the EHR system, no data would be recorded for these metrics. Since inpatient data were not studied, the true impact of EHR system use might be underestimated. The third reason for missing data is that the EHR system did not capture any data for certain metrics for all participating providers during certain months such as the In Basket messages received per day (missing data for all providers during April 2021, May 2021, July 2021, August 2021, and September 2021) and time in In Basket per appointment (April 2021) metrics. Neither we nor the analysts from Epic Systems could determine the root cause of the missing data.

On the basis of these findings, we used a complete case analysis to address missing values [ 29 ]. The observations with denominator=0 were excluded. The weighted averages did not capture the missing values data. As each In Basket metric was considered individually, a provider had to have at least 1 month of data for a particular In Basket metric to be included in the metric analysis.

Data Ranges

The start date was November 1, 2019 (the date of launch of Connect Care), and the end date was July 30, 2022, for the In Basket metrics.

Depending on the available data for the metrics, the monthly reporting periods included in the analysis ranged between 14 and 33 months. The overall amount of data varied between 1528 (15.92%) observations for the time in In Basket per appointment metric and 2203 (22.95%) observations for the time in In Basket per day metric. The total number of observations for all included metrics was 9598.

Statistical Analysis

Data aggregation, analysis, and visualization were performed using SAS (version 9.4; SAS Institute) and Tableau (version 2021.4.3; Tableau Software, LLC) [ 43 , 44 ]. The numerator and denominator from each metric were used to calculate the weighted daily means of all participants and each group.

A 2-sample t test (2-tailed) was used to compare the weighted daily mean of every metric for the medical FTE >0.5 group with the medical FTE ≤0.5 group and compare those of the medical FTE >0.5 group with the surgical FTE >0.5 group. A weighted average calculates the mean of a data set while considering the varying importance or significance of each number within the set. This approach is commonly used in statistical analysis. It is a critical tool for addressing fluctuations, managing uneven or distorted data, and ensuring fair representation of similar data points based on their respective weights.

In time-series analysis, such as the one we have conducted, time-weighted averages were used because the time series was not evenly sampled. Ideally, data points in a time series are evenly spaced, such as hourly, daily, or monthly intervals, where each point carries equal weight. However, in our data set, reporting periods were irregular, with varying lengths ranging from 27 to 35 days. Consequently, these reporting periods had different weights. To address this, we converted the reporting periods to a daily scale, ensuring each data point carried equal weight. In summary, a time-weighted average assigns weight to each value based on its duration relative to surrounding points, leading to significantly improved accuracy in the final calculation.

Trend analysis was used to evaluate the use trends over time to determine changes in Connect Care use by the participating providers. A simple moving average (SMA) curve was used to explore the learning curves (changes over time) for each metric [ 45 , 46 ]. A linear trend line was fitted to the SMA curve for each group (ie, medical FTE <0.5, medical FTE >0.5, and surgical groups) based on each included metric to determine the changes in trends (ie, whether the slope increased, decreased, or remained unchanged).

In all these analyses, a P value of <.05 was considered statistically significant.

Participant Characteristics

In total, 71 providers were included in the analysis. Of the 71 providers, 29 (40%) were women providers and 43 (60%) were men providers. The analysis did not compare results by age or gender because the numbers were small. The largest specialty group was internal medicine (n=14, 20%), followed by nephrology (n=10, 14%) and general surgery (n=9, 13%). The least represented specialties were dermatology, intensive care, neurosurgery, and cardiac surgery, at about 1% (n=1) each. Due to the diversity of specialties, the providers were grouped into a medical group (n=53, 75%) and a surgical group (n=18, 25%) based on previous similar studies ( Multimedia Appendix 1 ) [ 41 , 42 ].

Furthermore, the self-reported FTE was used to further subgroup participants. Of the 53 participants in the medical group, 27 (51%) participants reported FTE <0.5 and 26 (49%) participants reported FTE >0.5. All 18 (100%) surgical specialists reported FTE >0.5. This resulted in 3 subgroups: medical FTE <0.5, medical FTE >0.5, and surgical (all FTE >0.5) groups.

Overall Results

Table 1 shows the weighted daily means for all participating providers (including weighted daily means for the medical and surgery groups) for each metric in this study. The use of weighted daily means indicates a more precise method for determining the average appointments per day compared to a simple average based solely on the number of providers and reporting periods. In this study, because the reporting periods varied in duration, they were assigned different weights based on the number of days within each period. This adjustment ensured a more accurate representation of daily appointment averages.

On the basis of the weighted daily means adjustment, each provider had, on average, 8.31 appointments per day during the entire reporting period. The providers received, on average, 21.93 web-based messages per day and spent 7.61 minutes on average in the time in In Basket per day metric and 1.84 minutes on average in the time in In Basket per appointment metric. The time for the providers to mark messages as “done” (meaning that they had completed tasks associated with them; Turnaround Time) was, on average, 11.45 days during the reporting period. Although the surgical group had, on average, approximately twice as many appointments per scheduled day, they spent considerably less “connected time” (based on almost all time metrics) than the medical group. However, the surgical group took much longer than the medical group to mark messages as done (Turnaround Time; Table 1 ).

Table 2 shows the weighted daily means per provider group (ie, medical FTE <0.5, medical FTE >0.5, and surgical groups) for each metric in this study. According to the raw data, the medical FTE <0.5 and the surgical groups had, on average, more appointments per day during the reporting period than the medical FTE >0.5 group. In addition, all the time metrics indicate that the medical FTE <0.5 group had less time on Connect Care than the medical group FTE >0.5. The same was observed between the medical FTE >0.5 and the surgical groups, except for the turnaround time metric ( Table 2 ).

a Surgical group versus medical FTE >0.5 group comparison: P value=.07.

Trend Analysis

Table 3 presents the results of the trend analysis. All 3 groups had a statistically significant increase in the appointments per day and turnaround time metrics over the study period.

As presented in Table 3 , for the medical FTE ≤0.5 group, the appointments per day , In Basket messages received per day , time in In Basket per appointment , and turnaround time metrics showed statistically significant changing slopes (increasing trends over time), while the time in In Basket per day metric remained unchanged. The largest slope for this group was observed for the turnaround time metric with a value of 0.0055.

For the medical FTE >0.5 group, all metrics showed statistically significant changes ( Table 3 ). This group showed the largest number of statistically significant trend changes among the 3 studied groups. A total of 3 metrics (ie, appointments per day , In Basket messages received per day , and time in In Basket per day ) that showed statistically significant changes had increasing trends, while the time in In Basket per appointment metric showed statistically significant changes with a negative slope (decreasing trend).

a Increasing: positive slope and P value is statistically significant.

b Unchanged: P value is not statistically significant.

c Decreasing: negative slope and P value is statistically significant.

For the surgical group, the appointments per day and turnaround time metrics showed a statistically significant increasing trend, while the time in In Basket per day and time in In Basket per appointment metrics showed a statistically significant decreasing trend.

Although there were increasing and decreasing patterns among the included metrics, there were no obvious patterns across metrics and among groups. Therefore, there does not seem to be evidence of a “learning curve,” which would have shown a consistent reduction in time spent in the EHR system over time due to familiarity and experience.

Findings by Metric

The following sections describe the findings for each metric.

Appointments Per Day

During the reporting period, the weighted daily average number of appointments per day was 8.31 (95% CI 8.27-8.35) for all providers. For the medical group, the daily weighted average was 6.41 (95% CI 6.39-6.44), while for the surgical group, this number was 14.01 (95% CI 13.93-14.10) appointments per day. The weighted daily mean for the medical FTE ≤0.5 group (mean 6.47, 95% CI 6.44-6.49), compared to the mean for the medical FTE >0.5 group (mean 6.36, 95% CI 6.33-6.39), was significantly different ( Multimedia Appendix 2 ).

Although the slope changes were subtle, the SMA trends for the appointments per day metric for all 3 groups were statistically increasing over time ( Multimedia Appendix 2 ).

In Basket Messages Received Per Day

The weighted daily mean of web-based messages received was 21.93 (95% CI 21.64-22.22) messages for all 71 providers. The weighted daily mean for the medical FTE >0.5 group was significantly larger than that for the medical FTE ≤0.5 group. Furthermore, the difference between the weighted daily mean values of the medical FTE >0.5 group (mean 23.29, 95% CI 22.70-23.57) and the surgical group (mean 21.70, 95% CI 21.45, 21.94; P <.001) was statistically significant ( Multimedia Appendix 2 ).

In June 2021, Signal data recorded that 1 particular specialist received an unusually large number of In Basket messages. After an examination, it was determined that this was due to the EHR system sending a batch of all laboratory results from many patients to this particular medical specialist, who was probably on call. The spike from this individual’s data is reflected in the 2 graphs related to the medical group (FTE >0.5) and the graph for all providers ( Multimedia Appendix 2 ). While this particular case may be seen as an outlier, it serves as an illustration of what can potentially happen within an EHR system. Instances like this one may not be uncommon.

According to the SMA trend analysis, both medical groups experienced statistically significant increasing trends in this metric, while the surgery group’s trend remained statistically unchanged. The trend change was much more pronounced for the medical FTE >0.5 group (slope=0.0194) than that of the medical FTE ≤0.5 group (slope=0.0047). Notably, for the medical FTE >0.5 group, this metric had the largest slope and was the fastest changing over time ( Multimedia Appendix 2 ). This might have been because of the “anomaly” of a single physician in the medical FTE >0.5 group receiving a very large number of emails, as described in the previous paragraph. Furthermore, these results show the situations that are possible within the EHR system and need to be recognized.

Time in In Basket Per Day

The weighted daily mean for all providers was 7.61 (95% CI 7.59-7.64) minutes in In Basket per day. The weighted daily mean for the medical group was 8.86 (95% CI 8.84-8.89) minutes in In Basket per day, while that for the surgical group was 3.95 (95% CI 3.92-3.97) minutes per day. The medical FTE ≤0.5 group’s weighted daily mean was 7.98 (95% CI 7.95-8.02) minutes in In Basket per day, and the weighted daily mean for the medical FTE >0.5 group ( P <.001) was 9.73 (95% CI 9.68-9.77) minutes per day. The surgical group spent less time in In Basket per day than the medical FTE >0.5 group (mean 3.95, 95% CI 3.92-3.97, vs mean 9.73, 95% CI 9.68-9.77; P <.001; Multimedia Appendix 2 ).

On the basis of the trend analysis, the medical FTE >0.5 group showed a statistically significant increasing trend for this metric, while the surgery group showed a statistically significant decreasing trend and the medical FTE ≤0.5 group’s trend stayed statistically unchanged. While the result of trend analysis for this metric is different for each group, it is important to note that the slopes for each group were very small and clinically insignificant ( Multimedia Appendix 2 ).

Time in In Basket Per Appointment

After analyzing 1528 observations related to the time that providers spent in the In Basket per appointment, the total average time for both surgical and medical groups was 1.84 (95% CI 1.83-1.85) minutes. The surgical groups spent 0.60 (95% CI 0.59-0.61) minutes, while the medical group spent 2.78 (95% CI 2.77-2.79) minutes. The weighted daily mean for the medical FTE ≤0.5 group was significantly different compared to the mean for the medical FTE >0.5 group (mean 2.69, 95% CI 2.68-2.71, vs mean 2.88, 95% CI 2.86-2.90; P <.001). Furthermore, a significant difference was observed when comparing the medical FTE >0.5 group (mean 2.88, 95% CI 2.86-2.90) and the surgical group (mean 0.60, 95% CI 0.59, 0.61; P <.001; Multimedia Appendix 2 ).

For the time in In Basket per appointment metric, the medical FTE ≤0.5 group was the only group that saw a statistical increase in their use over time. The other 2 groups showed a statistical decrease in their use over time for this metric ( Multimedia Appendix 2 ).

Turnaround Time

Turnaround time is a metric group under the In Basket category within Signal. It reports the average number of days a provider takes to mark a message of a specific type as “done.” According to the data, the surgical group spent 16.22 (95% CI 14.69-17.76) days on average to mark messages as done. The medical group spent, on average, 9.72 (95% CI 9.21-10.23) days to mark messages as done ( Multimedia Appendix 2 ). For this metric, a significant difference was observed when comparing the 2 medical groups and between the medical FTE >0.5 and the surgical group. The study team was unable to identify the reasons for the delays.

For this metric, spikes in recorded data were observed ( Multimedia Appendix 2 ) for 2 study participants (1 medical and 1 surgical specialist) over several months, indicating extremely long delays in marking received messages as “done.” An explanation for these anomalies in data capture within the turnaround time metric remains elusive. Once more, we encounter an outlier; nonetheless, it serves as an example of potential EHR system use scenarios.

On the basis of the SMA trend analysis, all 3 groups experienced statistically increasing trends over time for their turnaround time metric ( Multimedia Appendix 2 ). The largest slope (0.0175) belonged to the surgical group and the smallest slope (0.0055) belonged to the medical FTE ≤0.5 group for this metric.

Principal Findings

Implementing Connect Care by AHS has transformed how providers capture and share information by establishing changes to workflows, processes, and charting approaches [ 47 ]. While the overall objective is to establish uniformity in the EHR system’s use, this study has revealed disparities in the timing of task completion within the EHR system. Furthermore, in certain cases, outliers have emerged whose use patterns are not easily explained with the existing data. This study revealed significant gaps in our understanding of EHRs and In Basket management, highlighting the need for further exploration and comprehension in these areas.

Khairat et al [ 48 ] evaluated the time spent by general and specialist pediatricians performing clinical documentation and In Basket tasks outside work hours. Specialists spent more time in the EHR system, and “this may be because specialists see more complex patients and, therefore, need more time to review the patient chart and to respond to In Basket messages” [ 48 ]. Although in our study, we cannot say what percentage of workload the providers spent on In Basket activities, we identified that they spent 7.61 minutes in the time in In Basket per day metric and 1.84 minutes in the time in In Basket per appointment metric . According to the raw data, the medical FTE <0.5 group and the surgical group had, on average, more appointments per day during the reporting period than the medical FTE >0.5 group. It would be valuable to explore the main workflow drivers of In Basket time and try to optimize efficiency in this area for all specialties.

The proportion of time spent in the EHR system based on the included metrics between the providers within the medical groups (FTE ≤0.5 and FTE >0.5) was similar; however, little can be concluded about the similarities or differences in use due to the high variability within the specialties. Although data analysis showed statistical significance for all metrics, it is apparent that FTE made no difference to the workload between providers working (FTE ≤0.5 or FTE >0.5). Before comparing part-time medical providers with full-time ones, we could not definitively attribute the observed differences between the medical and surgical groups to the fact that some medical providers worked part time or to the fact that all surgical providers worked full time. Our study did not reveal important differences in In Basket metrics among medical specialists regardless of the clinical FTE . Significant differences were observed between medical and surgical colleagues. Presumably, these differences relate to broad differences in medical versus surgical consultation and their associated workflows.

When comparing the 3 groups, the medical FTE >0.5 group was more “connected” than the medical FTE <0.5 group and the surgical group when considering the time in In Basket per day and time in In Basket per appointment metrics. Although the surgical group treated more patients (on average, 14.01 appointments during the reporting period), they spent less time in In Basket per day and per appointment, so they were “less connected” than the 2 medical groups. Nonetheless, while the data do not provide a direct explanation for these differences, they do provide insight into the structuring of workloads. This insight is crucial for comprehending how various professionals use the EHR system and identifying areas where workflow enhancements could prove beneficial.

We identified several providers’ data that were outliers in terms of their EHR use. For example, 2 providers took inordinately long times to mark received web-based messages as done ( turnaround time ), which impacted the data on between-group differences. Furthermore, there was a medical provider who received an extremely high number of web-based messages in June 2021. Such outliers demonstrate that certain scenarios can significantly influence the averages of various metrics, leading to skewed results. It underscores the possibility that data generated by the EHR system may not always be accurate, emphasizing the need for discussions and considerations with EHR system vendors regarding EHR functionality and measures to reduce outlier occurrences. Future research with a more robust statistical approach should be conducted to delve deeper into addressing and mitigating anomalies in the data.

One factor that we identified in our study is that Connect Care did not capture all interactions due to various vendor-imposed rules (eg, 5 appointments per reporting period). Similarly, Cohen et al [ 49 ] identified issues with vendor-derived metrics and how different vendors calculated the same activities in different ways and identified that not all EHRs (vendors) drew information from audit log data, which led to the inability to provide the whole picture of provider’s interaction with the EHR system [ 49 ]. Therefore, using only vendor-derived metrics may miss important aspects of the true impact of the EHR system on users. In the study by Cohen et al [ 49 ], 1 participant stated that “if different EHR (vendors) are attacking the issue differently, you will get variations not related to burden but just how the math is done.” Documentation time for In Basket use must be captured completely with the intent to understand how In Basket contributes to the overall workload of providers. If EHR systems are being associated with burnout, In Basket messages could be a starting point for common ground around the discussion of how web-based messages should be delivered and managed [ 50 ].

Future Directions

On the basis of the results from this study, we identified several future studies that can build upon this study. This study was descriptive and did not explore the correlation between the included metrics and provider satisfaction and burnout due to EHR system use. The next step would be to conduct a study exploring the circumstances around the individual EHR data. It would be valuable to explore the main workflow drivers of In Basket use time and try to optimize efficiency in this area for all specialties. A qualitative study should be conducted to explore the variances between actual and perceived EHR system use. While data from this study do not provide a direct explanation for these differences, they do provide insight into the structuring of workloads.

Furthermore, future studies should focus on the difference between providers with part-time and full-time clinical schedules and how that translates into EHR use. This insight is key for understanding how various professions use the EHR system in order to identify areas where workflow enhancements could prove beneficial. Moreover, future research should explore EHR use between different specialties and whether these specialties impact EHR use habits. In addition, studies should explore the association between other metrics and quality outcomes. Finally, future studies need to work on developing strategies for EHR data quality appraisal. In our study, we identified that the data generated by the EHR system may not always be accurate, emphasizing the need for discussions and considerations with EHR vendors regarding EHR functionality. In future studies with a more robust statistical approach, there may be an opportunity to delve deeper into addressing and mitigating anomalies in the data.

Limitations

This study has several limitations. The analyzed data were only the participating providers’ ambulatory (outpatient) data. Inpatient data were not included, which might have provided additional information on some of the metrics (inpatient data were unavailable for all included metrics in this study). Another limitation is the underestimation of some metrics based on how Epic defines and captures activity (eg, a provider needs at least 5 appointments scheduled per week within the reporting period and inbox activities related to phone calls or chart review). Furthermore, to address some In Basket issues, a person may need to access other parts of the EHR system to gather more information or complete some other task (eg, write a prescription) and only then go back into the In Basket to sign off on it. Therefore, the actual time in the In Basket is a systematic underestimation of the actual time it took to complete a task.

Due to the high variability of specialties (19 in total) and the low number of recruited providers for each specialty (ranging between 1 and 14 providers), we were unable to explore and compare the differences in EHR use between the specialties. The small number of participants might have created a bias regarding the reasons for participation. Another study limitation was that the FTE was self-reported, which might have led to providers over- or underestimating their clinical schedules. The final limitation is that we did not evaluate the types of web-based messages that the providers received in the In Basket. As this is one of the first studies evaluating Connect Care, we deemed that the focus should be on the overall metrics rather than the submetrics or categories.

Conclusions

This study demonstrated the enormous promise of the ability to harvest data from an EHR that describes system use and the potential impact that it has on the workflow of physicians. To take complete advantage of this, there must be an appropriate understanding of how EHR systems capture and measure the use by providers. This would be foundational to forthcoming studies examining the association between provider wellness and EHR systems in Canadian settings and studies focused on improving the EHR system’s user experiences, developing best practices for EHR systems rollout and subsequent use, and understanding how the interface of the user and the EHR systems interrelate. Although this study does not show how the included metrics could be used as predictors of providers’ satisfaction or feeling of burnout, the use trends could be used to start discussions about future Canadian studies needed in this area.

Acknowledgments

The authors would like to acknowledge and thank the providers who participated in this study. Without their help, this research would not have been possible. The authors would also like to thank the staff from Alberta Health Services and Epic Corporation for their willingness to provide the required data. Furthermore, the authors would like to thank the Canadian Institutes of Health Research (grant 180993) for providing funding for this work. Without this funding support, this study would have not been possible.

Conflicts of Interest

None declared.

Grouping of included providers according to specialties.

Findings by metric.

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Abbreviations

Edited by K Williams; submitted 10.10.23; peer-reviewed by M Zaidi, N Shaw; comments to author 26.01.24; revised version received 12.03.24; accepted 19.03.24; published 29.04.24.

©Melita Avdagovska, Craig Kuziemsky, Helia Koosha, Maliheh Hadizadeh, Robert P Pauly, Timothy Graham, Tania Stafinski, David Bigam, Narmin Kassam, Devidas Menon. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 29.04.2024.

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.

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

Published on 2.5.2024 in Vol 8 (2024)

An Exploration of the Goodness of Fit of Web-Based Tools for Māori: Qualitative Study Using Interviews and Focus Groups

Authors of this article:

Original Paper

• Liesje Donkin 1 * , BSc, MSc, PGDip (Health Psychology), PGDipArtsDist (Psychology), PGDip (Clinical Psychology), PhD   ; 
• Marie-Claire Bidois-Putt 2 *   ; 
• Holly Wilson 2 , BSocSci (Hons), MSc   ; 
• Penelope Hayward 1 , BA, MA, PGDipArts (Psychology), PGDip (Psychological Practice)   ; 
• Amy Hai Yan Chan 2 , BPharm (Hons), PhD  

1 Department of Psychology and Neuroscience, Auckland University of Technology, Auckland, New Zealand

2 School of Pharmacy, The University of Auckland, Auckland, New Zealand

*these authors contributed equally

Corresponding Author:

Liesje Donkin, BSc, MSc, PGDip (Health Psychology), PGDipArtsDist (Psychology), PGDip (Clinical Psychology), PhD

Department of Psychology and Neuroscience

Auckland University of Technology

Akoranga Drive

Auckland, 0627

New Zealand

Phone: 64 21847886

Email: [email protected]

Background: Indigenous communities often have poorer health outcomes and services under traditional models of care. In New Zealand, this holds true for Māori people who are tāngata whenua (the indigenous people). Several barriers exist that decrease the likelihood of indigenous communities often have poorer health outcomes and poor service fit under traditional models of care, including access issues, systemic and provider racism, and a lack of culturally safe and responsive services. Web-based interventions (WBIs) have been shown to be effective in supporting mental health and well-being and can overcome some of these barriers. Despite the large number of WBIs developed, more investigation is needed to know how well WBIs fit with an indigenous worldview and how they meet the needs of indigenous communities so that a digitally based future does not drive social and health inequities.

Objective: This study aims to explore the goodness-of-fit of WBIs of Māori individuals, the indigenous people of Aotearoa/New Zealand.

Methods: We used interviews (n=3) and focus groups (n=5) with 30 Māori participants to explore their views about WBIs. Interviews were analyzed using reflexive thematic analysis by members of the research team.

Results: Overall, there was a perception that the design of WBIs did not align with the Māori worldview, which centers around people, relationships, spirituality, and holistic views of well-being. A total of 4 key themes and several subthemes emerged, indicating that WBIs were generally considered a poor fit for Māori. Specifically, the themes were as follows: (1) WBIs are disconnected from the core values of te ao Māori (the Māori worldview), (2) WBIs could be helpful in the right context, (3) there are significant barriers that may make it harder for Māori to use WBIs than other groups, and (4) ways to improve WBIs to help engagement with Māori.

Conclusions: While WBIs are often considered a way to reduce barriers to care, they may not meet the needs of Māori when used as a stand-alone intervention. If WBIs are continued to be offered, developers and researchers need to consider how to develop WBIs that are responsive and engaging to the needs of indigenous communities rather than driving inequities. Ideally, WBIs should be developed by the people they are intended for to fit with those populations’ world views.

Introduction

Web-based interventions (WBIs) are therapeutic interventions delivered over technological means such as mobile phones or the internet to facilitate behavior change to improve health or well-being. Over the past 2 decades, the explosion in the number of WBIs has paralleled the growth and increased access to technology worldwide. This growth is driven by the potential of WBIs to reach more people at a lower cost than comparable face-to-face therapies. Several reviews now report that WBIs are as effective as face-to-face interventions [ 1 - 4 ], making WBIs a viable alternative to conventional therapies.

Indigenous communities often face more significant barriers to accessing health care than the dominant cultural group [ 5 - 9 ]. The barriers include systemic racism [ 10 - 14 ], negative attitudes held by health care professionals about indigenous people and traditional methods of healing [ 14 - 17 ], a lack of culturally responsive and safe services [ 18 - 20 ], and significant psychosocial barriers that make even getting to appointments difficult [ 21 ]. Due to these barriers and the social inequities created by colonization [ 22 - 24 ], indigenous people often experience worse health and social, psychological, and mortality outcomes. Given the potential benefits of WBIs to overcome some of these barriers, WBIs are increasingly being developed for the hard to reach or engage groups [ 25 - 27 ], such as indigenous communities.

However, one of the reasons that traditional health care underserves indigenous communities is that the care delivery model is often driven by a Western or dominant cultural paradigm [ 17 ], which often does not fit with the values and worldview of the indigenous community [ 28 , 29 ]. Thus, existing Western and biomedical models may further perpetuate health inequities [ 30 ]. For example, many indigenous communities such as Māori (the indigenous people of New Zealand) [ 31 ] hold a collectivist worldview rather than the individualized view prevalent in Western medicine [ 31 , 32 ]. In te ao Māori (the Māori worldview), the relationship and generational history between 2 people are paramount and influence engagement with others. Similarly, wairua (spirituality), Mauri (life force), whakapapa (history), and tikanga (ways of doing things) are paramount in all interactions. These factors are often deprioritized in the medical model, where an adequate clinician-patient relationship is assumed. Similarly, there is often a belief that this relationship will progress within what is largely superficial, impersonal and not responsive to the patient’s cultural needs nor recognizes the history and impact of colonization [ 33 ].

As the uptake and persistence of WBIs are often considered low, particularly on a long-term basis [ 34 , 35 ], it is essential to understand the views of potential users to inform the design and determine if WBIs are the best form of intervention. This has been frequently explored with nonindigenous populations, which found that time constraints and lack of perceived worth of the WBI were key reasons for stopping using WBIs [ 35 ]. In indigenous communities, uptake of WBIs has been found to be variable and can be affected by intervention characteristics [ 36 , 37 ]. In addition to this, many indigenous communities have poor access to technology and technological infrastructure [ 38 ], which can create further barriers to accessing WBIs. Understanding barriers to uptake can help researchers, developers, and policy makers consider how to improve WBIs for indigenous populations or if WBIs are even an appropriate intervention strategy for indigenous communities—because without this understanding, investment in technology may further drive health inequities.

Currently, there is limited research exploring the views of Māori adults about WBIs and the fit of WBIs with te ao Māori. Given this, this study sought to understand the views of Māori adults about WBIs using a qualitative design and reflexive thematic analysis of interviews and focus groups.

This study used a qualitative methodology using a mix of web-based videoconferencing interviews, face-to-face interviews, and focus groups. All interviews were facilitated by MCBP, who also recruited participants using snowball recruitment and social media advertising.

Participants

People were recruited into the study if they identified as Māori, were aged >18 years, and could consent to participation either orally or in a written format. There were no exclusion criteria for this study.

Recruitment

Participants were recruited through a range of recruitment methods using convenience sampling. Participants for 1 focus group (n=8) were recruited through Te Kete Pounamu, a nationally based organization for Māori with lived experience of mental distress and addiction. The remainder of the participants were recruited via the researchers’ professional and personal networks, web-based advertisements, and through relationships formed in the community by research team members.

The participants were offered a choice of participating in an individual interview or a focus group discussion. During the initial stages of the meeting with the participants, the interviewer (MBP) opened the session with karakia (prayer) if the participants wished for this to happen. The interviewer then engaged with participants in whanaungatanga (building relationships through shared connection). The participants were then reoriented to the study, and consent was obtained in a written or recorded verbal format. The participants then confirmed that they were happy to be recorded, and the interview or focus group commenced.

Interviews initially started by providing the participants with a definition of WBIs and then asking participants about their views of WBIs. If needed, the participants were prompted to talk about why they felt WBIs did or did not fit with their worldview. A specific example of an existing WBI was used as a talking stimulus by demonstrating the WBI if required. At the end of the session, participants were thanked for their time, and the session was closed with karakia if it was opened with one.

Data Gathering

Given that Māori have a strong oral history and prefer to engage kanohi-ki-te-kanohi (face-to-face), interviews and focus groups were used. Combining both methods meant that participants could be part of a group to share their views or talk individually. Consultation with Te Kete Pounamu indicated that their tāngata whaiora (people seeing health) would likely prefer to engage in a focus group. Thus, focus groups were offered to allow this. Individual interviews were offered to enable flexibility in interview times at a time and place that suited individuals. Group interviews were complete kanohi-ki-te-kanoi, while individual interviews were a mix of kanohi-ki-te-kanohi and on the web. Both interviews and group interviews used the same semistructured interview guide with questions to facilitate reflective discussion. Examples of questions used were as follows: “What are your views on digital interventions to support mental health and well-being?” “What do you think has led to you developing these views?” “How does the use of digital interventions fit your background and culture?”

A total of 8 transcripts were produced, consisting of 3 individual interviews and 5 interviews with >2 participants. A total of 30 people were interviewed as part of the study. The interviews were manually transcribed (by HW); checked (by LD); and coded using inductive reflexive thematic analysis by Clarke et al [ 39 ] by 2 members (LD and MCBP) of the research team individually and following a 6-step process of familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Reflexive thematic analysis was chosen as an appropriate methodology for this study as it is aligned with recent projects seeking to understand the views of Māori [ 40 - 43 ] as tāngata whaiora (those seeking health).

The transcripts were not returned to the participants for review. Any disagreements in coding between LD and MCBP were resolved by discussion between LD, MCBP, and PH. Codes, subthemes, and themes were combined on the web for review by the rest of the research team using a visual collaboration platform [ 44 ]. The wider team (LD, MCBP, HW, PH, and AHYC) further discussed the codes and themes.

Ethical Considerations

Ethics approval for the study was received from the Auckland Health Research Ethics Committee (AHREC AH23110; expiry October 18, 2024).

Written consent was provided by all participants who completed face-to-face interviews or focus groups following a review of the participant information sheet. In web-based interviews, an oral consent protocol was followed where participants were video recorded giving their consent. The researchers then completed the consent forms based on participant responses, and consents were electronically provided to the participants. The participants had the option to provide scanned copies of the signed consent form to the interviewer if they preferred this method over video consent.

One participant completed their interview or focus group, the recordings were transcribed (completed by MCBP or HW), and all identifying information was removed from the transcript. The deidentified transcripts were provided to the wider research team for review. LD led the review process with MCBP. Codes and themes were discussed with HW, AHYC, and PH once coding was complete. The original recordings were stored separately from the transcripts in a secure manner on a password-protected university-managed research drive aligned with the data management plan.

During the research process, Koha (a gift acknowledging the time spent by participants and sharing their knowledge) in the form of NZ $20 (US $11.87) supermarket vouchers and kai (food) were provided to participants.

A total of 30 participants participated in this research across 5 group interviews and 3 individual interviews ( Table 1 ).

In terms of participants, ages ranged from 18 to 74 years, with the mean participant age being 41.3 (SD 19.6) years. Of the 30 participants, 11 (37%) identified as a man, 12 (40%) as a woman, 6 (20%) did not specify their gender, and 1 (3%) identified as nonbinary. The participants lived in a mix of main urban centers, such as Auckland and Hamilton, and in more urban areas, such as Northland and Rotorua.

The participants were largely aligned with their views on WBIs. Although they often offered a critical perspective on WBIs, they also quickly indicated that others might find WBIs helpful even though they did not believe that WBIs would be useful for themselves. Through the analysis of the transcripts, 4 themes emerged, as listed in Table 2 .

These themes and subthemes are discussed in more detail in the subsequent sections.

a WBI: web-based intervention.

WBIs Are Disconnected From the Core Values of Te Ao Māori

Te ao Māori (the world view of Māori) and Māori culture are built around relationships, collectivism, and a shared connection. Considering the collective is vital, and a treatment model focused on the individual’s pathology alone without connection or relationships is at odds with Māori beliefs and values. The participants felt that WBIs may not understand and replicate the critical relational aspect, which is crucial in all aspects of life for Māori. Therefore, most participants did not consider WBIs as something that would fit with Māori culture or worldview. A participant said the following:

Because you are not face to face...and us Māori’s, we are...like, unless we know you and love you, we’ll never be as open though. No way! Ah, it’s, it’s, I don’t know...it just is [Group 5]

Another participant stated the following:

I definitely think doing something online definitely lacks whanaungatanga [relationships/the process of forming relationships] and that kind of personal relationships with your doctor or your medical provider [Group 5]

Many of the older participants struggled to conceptualize what an automated program such as a chatbot would look like and how this could be used to support well-being, as this was removed from the concept of relationships, which are key to wellness and healing. Even when considering web-based therapy delivery, this had significant barriers to developing whanaungatanga. For some, there was a perception that without a real in-person connection with others, it would be easy to mask true feelings and intentions, which would further limit the benefits of the WBI. Others felt that people might inadvertently disclose more than they wanted to due to the WBI not seeming like a relationship (and more like a diary), and this could be harmful due to mistrust in how this information would be used. The overall message is that a person’s absence meant the absence of connection and healing. A participant said the following:

For healing purposes, people are necessary in our culture [Group 1]

I know people, they would like the online thing because they don’t feel like they are disclosing much about themselves. They are, but they don’t feel like they are [Interview 2]

Another participant said the following:

At the same time, when its online you can kind of feel some like you’re wearing a mask. So, it’s not really you [Interview 3]

The perceived lack of connection for users with WBIs hindered honesty and information sharing. This ultimately limited the benefits and healing that could be obtained when exclusively using WBIs. For some participants, there was the belief that WBIs and the digital world contributed to poor health as people became more disconnected from others; lost their ability to communicate; and tended to live in a digital world that was disconnected from their whakapapa (family history or genealogy), whenua (land), atua (gods or spiritual beings), and values. Some perceived turning to a digital world for help as a lack of personal responsibility for healing, which would further exacerbate long-term difficulties and have implications for future generations. This was particularly emphasized for automated interventions with a lack of relationship and accountability. A participant stated the following:

I do I think there is a whole generation, like the younger generation, who will know nothing else but digital stuff and like babies who now are growing up seeing nothing but masks on people’s faces. It’s the same kind of thing they won’t know how to relate unless it’s seen, or read it, or hear it. You know, tap into it online. You know? Their connections might be doing the de-de-de finger scrolling online, whatever online that might be their connection. But for me, I’m old school. I like people [Group 1]

Te Ao Māori Is About Wairua and WBIs Cannot Replicate This

When Māori connect, they do so with an energy transfer between 2 people, which conveys many things, including meaning to nuanced words. As such, te ao Māori is about wairua (spirituality) and Mauri (energy or life force), which respondents felt could not be conveyed by a digital tool. The 1D nature of the WBIs often felt empty to participants, robotic, and therefore not healing. For some, there was a perception that turning to WBIs would further drive participants away from te ao Māori and may worsen the underlying illness mechanism. A participant in stated the following:

We need that human connection. But it’s not just physical connection. It’s the frequency, it’s the energy, it’s the Wairua [Group 1]

A participant stated the following:

Well-being for Māori comes from connection. I think well-being, particularly in terms of Māori well-being, you know? The energy and frequency that comes with healing...um...is can be misinterpreted; can be absent [Group 2]

The last thing you want is for people to be dependent on digital apps for their well-being. That would be disastrous when people don’t know how to go to other people in a group and don’t be empowered enough to heal themselves [Group 1]

Māori Models of Health Are Holistic, Whereas WBIs Are Singular in Focus

Many participants reflected on Māori models of healing as being holistic and considering all areas of well-being. Specifically, one was unlikely to become well or heal from illness by just considering 1 facet of treatment (eg, thinking styles), when being well also included good physical health, strong connections to whānau (family) and whenua (the land), whanaungatanga (relationships), a connection to a higher purpose and meaning, and spirituality, all of which were considered essential for people to flourish. The approach of WBIs was largely seen to be singular in focus and did not fit with te ao Māori views of what was needed for healing. It was recognized that the perceived approach of WBIs was not aligned with Māori models of health and was more aligned with traditional Western models of treatment, such as psychological therapy or a medical model.

The common themes demonstrating the desire for a holistic approach included being able to meet the needs of whānau through accessing food, forming relationships, and recognizing that conversations about Mauri and spirituality were absent. Even when WBIs incorporate te reo Māori (the Māori language), the use of kupu (words) are often perceived as tokenistic, with poor translation that fails to capture the nuances of te reo Māori.

WBIs Could Be a Strand in the Weaving of a Well-Being Kete

Despite the belief that WBIs were unlikely to meet the ideal delivery of an intervention to support the well-being of Māori, many participants were open to considering using a WBI. Specifically, there was a perception that WBIs could be used as an adjunct to a treatment or therapeutic intervention already underway, particularly when there was already a strong relationship with the therapist or the care team. A participant stated the following:

I feel like I could get the information I need to do more. Cause, obviously, an app can only go so far, and your mental health is really...you know? You’re paddling that waka [boat]...People can help you, but you’re the main navigator of that boat...I think it will help start it will help you build the waka and get it onto the water, um, but then yeah...I am guessing that you would have to put in the work yourself to go find other resources [Interview 3]

Am just not sure we would be able to actually heal people properly, but it could help at least getting them toward the resources they need [Group 3]

WBIs Could Be Helpful in the Right Context

The participants noted several strengths of WBIs and were open to WBIs being a tool that might benefit some people more than others. Context was important, as was the support around the WBI. Specifically, there was a view that while WBIs might be good for some people, using them in isolation, without connection to people, might actually exacerbate issues.

WBIs Could Be Useful for People Who “Moved With the Times”

There was a perception that WBIs might suit those that have moved away from traditional modes of healing, such as the younger generations, but those groups did not always support this view. Many participants reported that they felt “older,” meaning they were more likely to struggle with WBIs than younger generations. There was a strong sense that rangatahi (young people) would be more inclined to use WBIs than kāumatua (elders), who were less confident with technology. This was observed in this study by the difficulty many kāumatua experienced accessing the stimulus WBI and the confusion around what the application was asking. The participants often indicated that they did not believe they could learn new technology and tended to avoid using it. As such, the perceived stress of using the WBIs outweighed the potential benefits. A participant stated the following:

I think the young Māori people might have embraced it, but the older ones, we say, “It’s pākehā! [non-Māori].” They won’t; they won’t switch onto it. Could you imagine your mother doing it? [Interview 2]

Younger participants tended to voice more concerns about functionality and technological issues with WBIs and less about the values disconnection expressed by the older participants. While open to using WBIs, the younger participants still had reluctance to do so. A low tolerance for poor functionality was indicated, with participants highlighting that the complicated log-in processes or errors would lead to them feeling frustrated and uninstalling an application. This indicated that significant barriers to use were complicated sign-up or sign-in processes and outdated or faulty technology. A participant stated the following:

I feel like I would probably uninstall it. I mean, I don’t really have the patience for broken web pages and things...I don’t think most people do. Especially on phones and your meant to be doing a million things at once [Group 3]

The younger participants also supported the views of the older participants around holistic care and the desire for a face-to-face connection. Thus, the assumption that WBIs were embraced by rangatahi was not necessarily true. The younger participants wanted interpersonal connection and did not think WBIs could meet this. WBIs were instead seen as a source of information but unlikely to be considered for therapeutic intervention or connection with others.

Apps Could Be a Tool but Not the Solution: a Blended Care Approach Is Needed

Many participants felt that they would be more open to using a WBI when integrated in the context of an existing, trusted relationship. The perception was that the role of the relationship would be to facilitate healing, while the WBI could serve for monitoring, support, providing information, promptly answering questions, and potentially facilitating connections to others beyond the primary the relationship. The relationship would center around wairua, whanaungatanga, and whānau, while the WBI might provide exercises, reminders, and ways to keep track of important things. Relaxation and mindfulness recordings were specifically noted as something that could be provided or used by participants in WBIs. Contacting a trusted support or therapist through the WBI was also seen as a way to increase whanaungatanga and healing if used in a timely and appropriate manner.

Barriers to Using WBIs for Māori

There were several barriers to using WBIs for participants, and many participants shared that they believed other Māori would also experience some of these challenges.

WBIs Come With an Upfront Cost That May Drive Inequitable Access

Having the technology to run WBIs includes an internet connection, an internet-enabled mobile phone or computer, and ongoing data. Cost was a barrier for many participants, who often did not have data on their phones or relied on free Wi-Fi connections in public spaces. Some participants spoke of the challenge of feeding their family and that this would take priority over the financial cost of using a WBI. This was evidenced by the questions around the app’s functionality and if it had features that would enable them to access food for their whānau (family).

Technical Issues Put People Off

Technical issues were a frequent complaint and often discouraged people from continuing to use a WBI. This was particularly salient when the difficulties were related to logging into a platform for the first time. Many of the older participants felt confused by log-in demands, and this initially created the perception that they would not be able to use WBI as it was too “technical” for them. The participants felt that the upskilling required to use a WBI would outweigh the benefits, particularly when there was already a belief that WBIs were not a good fit.

Literacy and Language May Make Engagement Difficult

At least 1 participant spoke of challenges with reading and writing and said that many of the WBIs available were text heavy and did not cater to people with reading difficulties. Language was particularly an indicator of this, with low use of te reo Māori kupu (words), the selection of only limited imagery of Māori people or images that would resonate with Māori, and a lack of clear use of Māori concepts and frameworks in the tools. When imagery or words were used, they were often inappropriate or superficial. The language and tone used were also considered important, with some older participants identifying that web-based communication was harder for them to understand. Several participants asked if the WBI was available in te reo Māori, as this was a preference. However, there was recognition that many Māori are still regaining te reo Māori; therefore, to be accessible, WBI should offer a range of te reo Māori options, from having key words in te reo Māori to being fully in te reo Māori.

Mistrust Due to Years of Systemic Racism and Broken Promises Create Barriers

Several participants reported feeling suspicious of the whakapapa (history and process of coming to be) of WBIs and the research. There were concerns about who was behind the WBI design or who was communicating with participants through the WBI and what their motivation was. The participants appeared to have a lack of confidence that the WBIs would maintain their privacy and instead felt that information may be shared with agencies that might later penalize users.

On a simple level, the participants often did not trust that the person behind the WBI or the web-based therapy was invested in talking to them. There were concerns that the person could be doing other things while engaging in the healing conversation or that the person using the WBIs would not be a priority. A participant stated the following:

I share something you share something you know that kind of kōrero or that I don’t know if they’re listening. I mean, while I’m talking, what are they doing? Emailing somebody? [Group 1]

The participants raised further concerns about the confidentiality of information shared with WBIs and how this would be used, particularly with regard to information potentially being shared with government agencies and the potential negative impact this could have on the participant and their whānau. This mistrust was not limited to WBI but to the researchers, the institutions, and the groups that supported it. A participant stated the following:

What puts me off is that there is nothing that comes across as [Māori]. This is a pākehā app for pākehās, pākehās solutions, pākehā reference pākehā, you know? And I think that’s one of the, um, big issues of todays, um, well-being solutions is that pākehā are trying to form solutions using methodologies that aren’t Māori, you know? [Group 1]

Many participants reported struggling with interventions that Pākehā developed as they lacked the depth of understanding about the historical impact of colonization on Māori and how this may impact why the person is seeking help. Many felt that interventions and Pākehā therapists and WBIs could not address this as they did not have these lived experiences. A participant stated the following:

I would want someone who can understand what lived experience is... [Group 2]

I prefer to speak with people, real people. And when it comes to like, um, some of my historical stuff, then I would want someone who can understand what lived experience is. You know, email doesn’t cut it either, you know, again who is this person that I’m emailing? [Group 1]

Mistrust was further exacerbated by the perception that Māori did not design many WBIs for that were reportedly for Māori. This was not only due to the WBI being perceived as a poor fit for te ao Māori but also due to Pākehā ultimately designing many WBIs for Pākehā (or the dominant paradigm) and then adapting (often poorly) these for Māori. Therefore, very few interventions were specifically designed for Māori, and even fewer were created by Māori. A participant said the following:

There’s no Māori kupu in here. Not even a “kia ora,” welcome [Group 1]

They had a real photo, but, um...whoever created didn’t look at the photo properly and it had somebody poking their tongue. So what time was given to that? I felt it was lacking big time cause if I’m not in a great space and I see somebody poking their tongues, saying, you know? I’m feel challenged! What’s up with that? [Group 1]

The participants were also wary of research teams that used a Māori interviewer but did not involve Māori in key decision-making. Questions were raised around the motivations of the teams and whether they came from genuine caring and a desire to reduce inequities (it was noted that the interviewer was challenged about this on >1 occasion). There was a belief that those teams that did not have genuine motivation would create solutions that were unlikely to resonate with Māori. A participant stated the following:

You know, they’re not using Māori or mātauranga Māori [knowledge] or Māori methodologies to create Māori solutions or do the research. You know they have got a brown face here, but I mean, who’s behind it and why they behind it? You know? [Group 4]

Ways to Improve WBIs to Help Engagement With Māori

Despite reservations about WBI, the participants provided several recommendations that could make WBIs more engaging for Māori. These were specifically about features of WBIs that would make them more engaging for Māori who may be interested in using WBIs.

Māori Imagery Is Key for Māori to Connect

The most common subtheme was that the participants wanted to see themselves reflected in the WBI. Imagery was important, and the participants wanted to see a mix of images, including pictures of Māori people, whānau (families), and hapu (communities). This imagery also included other key things such as bodies of water, native bush, maunga (mountains), Māori art and carvings, and buildings easily identified as Māori (eg, wharenui—meeting houses). This range of imagery tied back to a sense of holistic approaches to well-being, including the connection to people, to whenua (land), and to key important spiritual sites.

The participants also highlighted that Māori are diverse people and that not just 1 image connects with all Māori. Images depicting kapa haka or individuals performing a pukana (facial expression) were frequently viewed as oversimplified and occasionally offensive, simplifying the complexity of Māori culture. Instead, the participants would be drawn to WBIs that used a range of imagery of people showing the diversity of Māori. A participant stated the following:

A diverse range of Māori women. Um...sizes, ages...um, you know, skin tone. You know? Just like everything [Interview 3]

This was considered important by almost all participants, particularly as some participants identified that Māori are a strongly oral and visual culture with images of kowhaiwhai (weaving), whakairo (carving), and taonga (treasured items) drawing the eye and creating connection. For participants, many WBIs still mimicked clinical rooms in terms of the colors used and simple pages, which further created a sense of depersonalization and disconnection. A participant stated the following:

It seems very like clinical in a way, um, because of the coloring. Like, it’s all blue, like, it kind of just makes me think of, like, the doctors, um, like, it’s very formal I think [Group 3]

Māori Models of Well-Being Should Be at the Heart of All Interventions

Several participants noted that if WBIs interventions could be constructed around Māori models of well-being, WBIs would likely be more engaging for Māori. The key model suggested was Te Whare Tapa Whā [ 45 ]. While other participants did not explicitly outline a model, they articulated the need for a holistic approach to care that supported all aspects of wellness, including connection to culture as a path for healing. A participant stated the following:

Inclusive of the mental effort, you know the spiritual and all that stuff and the physical but so is this are they talking about holistic well-being or are they just talking about mental well-being because you can’t have one it’s, like, Te Whare Tapu Whā thing, ah, you can’t just focus on the one when you are expecting others to fall into place you have to work on the whole lot [Group 1]

There needs to be some sort of cultural, um, tool that helps...that grounds people. I guess whakapapa was one of those things [Group 1]

Improved Integration of the Te Reo Māori Language Would Make WBIs More Appealing

The participants agreed that correct and appropriate use of te reo Māori was important if WBIs were to engage Māori. Recognizing that the fluency of te reo varied, the participants wanted WBIs to be able to be modified based on the user’s fluency (such as entirely in te reo Māori or with only a few words). Even for those only beginning their te reo journey, keywords such as a greeting should be used meaningfully. The participants also noted that, based on past experiences, simply translating a few words into te reo lost the nuances of the language and could lead to misunderstanding and, at times, felt tokenistic. There was an emphasis on the correct use of Māori words, which often carry contextual meanings. Tokenistic inclusion of these words can lead to misunderstandings or, at worst, be offensive. Therefore, people developing WBIs for Māori needed someone fluent in te reo Māori working on the content rather than using simplified translation tools.

By Māori for Māori

A strong theme that came through was that WBIs, interventions, and tools that were developed by Māori for Māori resonated more strongly with Māori participants and were more likely to be engaging and used. Trusted institutions and sites tended to result in resources that were more readily used. A participant said the following:

I have searched through...through Te Ora [website] you know to find out stuff I think because it’s a Māori organization there’s a sense of connection being a Māori organization [Group 1]

A clear understanding of the whakapapa (history and origin) of the research project and the research team was deemed crucial, ideally with the project being designed and led by Māori for Māori. In addition, there was discussion about incorporating key models of health and acknowledging the influence of significant Māori figures in the project’s development.

Principal Findings

This study is one of the first to explore the views of Māori, the indigenous people population of New Zealand, about WBIs. Although participants could see the potential benefits of WBIs when used in the context of a strong existing relationship, there were concerns that a digital tool would not be able to facilitate healing due to the perception of WBIs being 1D in their focus rather than holistic, the potential to drive people further away from te ao Māori, and the lack of genuine connection that could be made on the web. Significant barriers to using WBIs were highlighted, including the impact of social inequities, which hindered access to the technology needed to engage with WBIs. Educational disadvantage also contributed to difficulties, particularly with text-heavy platforms. In addition, concerns about confidentiality and mistrust in the motivations of researchers (and government) due to experiences of colonization were clear.

This study makes a unique contribution to understanding WBIs, how they fit with the view of Māori as the indigenous people of New Zealand, and how indigenous communities may perceive and respond to WBIs. Our findings are in contrast with previous studies that explored uptake and engagement with WBIs in indigenous populations and ethnic minority groups. A recent scoping review exploring the use and uptake of web-based therapeutic interventions among indigenous populations in Australia, New Zealand, the United States, and Canada found moderate uptake of WBIs and potentially improved health outcomes associated with them [ 36 ]. Of the 31 studies, 9 (29%) were from New Zealand, with 3 (10%) relating to 1 web-based cognitive behavior therapy–based intervention (Smart, Positive, Active, Realistic, X-factor thoughts), 1 (3%) relating to minimizing risky alcohol use, and others (n=5, 16%) on specific noncommunicable diseases. The review findings reported a more positive experience with WBIs, possibly because of the younger age of participants and the way the WBIs were implemented, for example, delivered in school-based settings or with significant input and coproduction with Māori. Similar to our findings, the review highlighted the importance of tailoring content and presentation formats to ensure cultural relevance; appropriateness; and a customizable, easy-to-use interface. Another systematic review looking at the use of digital technologies to improve the mental health and well-being of indigenous people reported 27 studies that generally support the effectiveness of digital technologies in aiding the provision of mental health services but acknowledged that decolonizing and culturally appropriate approaches are needed [ 46 ].

The studies in these reviews generally do not examine the user experience of using the WBIs in depth. The review was limited in the degree to which it could explore barriers to accessing WBIs resulting from cultural and linguistic diversity, low health literacy, limited digital capabilities, and infrastructural and resource limitations for individuals and communities in different geographic locations—concepts that our participants expressed as potential barriers to WBI use. Similarly, a recent rapid review examining the use of web-based care in indigenous populations highlighted several barriers to engagement with web-based care—cost, accessibility, digital literacy, and language [ 47 ]—which align with our findings. The review emphasized the importance of building relationships and trust and ensuring the infrastructure is present to support technology navigation with indigenous populations, echoing our findings. Our study builds on these reviews by being one of a few studies to examine the views of Māori using qualitative methodology, which may explain the more in-depth findings articulated by respondents.

Limitations

Several limitations for this study exist. First, although the sample size was large for a qualitative study, Māori, like all indigenous people, are diverse; therefore, the views reflected in this study may not apply to all Māori. These views can likely be beneficial in shaping the development of WBIs. However, an effective WBI for Māori should be developed by Māori with the understanding that one intervention will not fit everyone. Instead, WBIs need to be developed with Māori models of well-being being central for use alongside a strong kanohi-ki-te-kanohi (face-to-face) therapeutic relationship. Previous reviews [ 36 ] recognize that the definitions of health often used are less holistic and relational than indigenous models of health and well-being, which may affect the interpretation of published studies in this area or result in studies appearing to be more effective than what would be perceived from a more holistic framework.

Second, this study explored the views of WBIs and used an existing intervention as an example for participants. This means that interventions that may have a different development process (eg, Māori developed) may have resulted in different views by the participants around the acceptability and usability of WBIs. The views of the participants may also be affected by the stimulus chosen as the example.

Finally, while most of the research team were Māori, the researchers varied in the strength of their connection to te ao Māori and their knowledge of this. This means that the analysis of the transcripts and subsequent findings may have varied if other people with different understandings and connections were to analyze the transcripts.

Reflections

One challenge for the research team was to avoid replicating some of the concerns and barriers regarding WBIs that were raised by the participants. Specifically, one aspect was the composition of the research team, where the individual conducting participant interviews was Māori, while the senior authors of the paper were not Māori. For context, this project is 1 part of a 2-part study designed from the onset with a diverse team in consultation with a Kaupapa Māori nongovernment organization. MCBP was involved in early discussions with the research team about the project and was brought into the team in recognition of the mātauranga Māori (knowledge unique to Māori) that she brings. MCBP was supported to be an active and equal member of the research, including being provided with support to upskill in qualitative methodologies. PH and HW were engaged in reviewing the themes to ensure interpretation and understanding were correct and to add to the richness of the interpretation, as the research team did not want to assume understanding and acknowledged their limitations around the lived experiences of Māori. Author order was decided among the team (MCBP, HW, PH, LD, and AHYC) based on roles in the project, with the decision to make MCBP and LD joint lead authors in recognition of the different roles in the project. We recognize that Māori should ideally lead projects exploring the view of Māori, and the experience that MCBP has within this project has meant that she has been able to colead a subsequent project and is beginning to develop her own research pathway as a Māori researcher with lived experience.

Conclusions

Through in-depth qualitative interviews and focus groups with Māori (indigenous people of New Zealand), WBIs were found to be generally considered a poor fit for Māori as the design of WBIs did not align with the Māori worldview or concepts of well-being. This contrasts with previous findings, where WBIs have been shown to be effective in supporting mental health and well-being and can overcome some of the traditional barriers to help seeking. With the large number of WBIs being developed, these findings are important in highlighting key considerations for WBIs to promote engagement with Māori, particularly considering how well WBIs fit with the indigenous worldview and how they meet the needs of indigenous communities in a culturally appropriate manner. While WBIs may have a place in supporting the well-being of Māori, WBIs alone are unlikely to achieve the same benefits expected for a non-Māori population and may further drive health inequities if not properly implemented and supported.

Acknowledgments

The authors would like to acknowledge the participants who contributed to this study and Te Kete Pounamu for their support, advice, and help with recruitment for this project. In addition, funding was provided by Chorus and Oakley Mental Health Foundation.

Data Availability

The data sets generated and analyzed during this study are not publicly available due to participants not consenting to the sharing of data beyond the research team with consent given for publication.

Authors' Contributions

LD is a health and clinical psychologist and researcher of New Zealand European descent who works in the area of digital interventions. LD was involved in the design of the project, ethics application, recruitment, overseeing interviews and supporting MCBP to complete the interviews, checking the transcripts, overseeing data analysis including supporting MCBP to upskill in thematic analysis, coding and generation of themes, and writing the manuscript. MCBP is a Māori (Ngāti Whakaue and Te Arawa) and a research assistant for this project. MCBP was involved in the recruitment, facilitated interviews, and focus groups as well as the checking of transcripts, coding of interview data, and generation of themes. HW is a Māori (Ngāi Tahu) and a PhD candidate at the University of Auckland. HW was involved in the project as a transcriber of the interviews. HW was also involved in discussions with the wider research team about the themes uncovered in the data. PH is a Māori (Ngāti Porou, Ngapuhi, Te Whānau-a-Apanui) psychologist and an academic who is completing her PhD. PH was involved in the discussion where there was disagreement in the coding of the transcripts and was involved in the wider team discussion about the generated codes. AHYC is an academic clinical pharmacist with Asian heritage. She has particular expertise in digital health, big data, and behavioral medicine. AHYC was involved in the project development, reviewing the themes from the interviews, and contributing to the manuscript.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 29.06.23; peer-reviewed by Y Tong, P Roach, R Nieto; comments to author 04.09.23; revised version received 12.11.23; accepted 22.11.23; published 02.05.24.

©Liesje Donkin, Marie-Claire Bidois-Putt, Holly Wilson, Penelope Hayward, Amy Hai Yan Chan. Originally published in JMIR Formative Research (https://formative.jmir.org), 02.05.2024.

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