The Journal of School Nursing2022, Vol. 38(1) 74–83© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI: 10.1177/10598405211012680journals.sagepub.com/home/jsn
School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.
mental health, content analysis, young people, youth, depression, symptom taxonomy, scoping review
Depression is a severe public health concern in young people including adolescents and young adults. Approximately one in 10 people aged 12–25 experience depressive episodes, and the prevalence of depression has been increasing in recent years in the United States (Mojtabai et al., 2016; Weinberger et al., 2018). Despite the high prevalence of depression in young people (Auerbach et al., 2018; Ibrahim et al., 2013; Kempfer et al., 2017), evidence shows that 60% of adolescents and 30% of adults with a major depressive episode do not receive appropriate treatment (National Institute of Mental Health, 2019). Recognizing symptoms of depression is an essential step toward early diagnosis and intervention (Klein et al., 2020). However, recognition of depression in young people, especially in adolescents, may be challenging.
The younger population may exhibit symptoms of depression or depression-related behaviors that can look different from those reported by middle-aged adults or older adults (Rice et al., 2019; Trudeau et al., 2016), possibly due to their developmental stage or increased mood fluctuations associated with younger age groups (Moreh & O’Lawrence, 2016; Trudeau et al., 2016). In this study, we included subjective metrics, and objective metrics or behaviors, as “symptoms” to ensure a comprehensive picture of depression in young people. For example, depressed adolescents may have difficulty in controlling their temper and appear angry (Jackson et al., 2011; Orchard et al., 2017; Selph & McDonagh, 2019); some may report feelings of irritability rather than sadness (Selph & McDonagh, 2019). Further, young people with depression are more likely to miss school or work or to have substance abuse problems (Moreh & O’Lawrence, 2016; Walters et al., 2018).
School nurses have a positive impact on youth health. They provide care to children and adolescents in schools and young adults at colleges or universities and may provide care in the school setting for vulnerable populations such as students experiencing homelessness (Gultekin et al., 2020) or refugee children and youth who are at risk of developing mental health problems (Johnson et al., 2017; Sullivan & Simonson, 2016). Because school nurses are the most accessible health care providers to young people and on the front lines to identify those who may have mental health issues, including depression, it is important to support school nurses to recognize depression in young people (Law et al., 2017; Mattey, 2019).
Depressive manifestations (“symptoms”) are multidimensional. Individuals with depression exhibit symptoms that can be clustered into cognitive, emotional, interpersonal, and somatic dimensions (Cheung & Power, 2012), in addition to behaviors such as attempted suicide, as mentioned in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5; American Psychiatric Association, 2013). There are assessment tools available, such as a list of diagnostic criteria for depression (i.e., DSM-5) or depression-assessment measures (e.g., Patient Health Questionnaire 9 or PHQ-9; Beck Depression Inventory or BDI). However, most of them only cover typical symptoms of depression (e.g., sad, changes in appetites, and sleep patterns) or are constrained to specific dimensions (e.g., cognitive, somatic, and emotional; Cheung & Power, 2012). Although those depression-assessment measures demonstrated acceptable sensitivity and specificity (Borghero et al., 2018; Shean & Baldwin, 2008), some depression-related aspects in young people (e.g., being bullied or aggressive behaviors) may be overlooked. Interpersonal factors are crucial to assess in young people, and they are often missing from the commonly used depressionassessment measures (e.g., PHQ-9 and BDI) and DSM criteria (Cheung & Power, 2012).
Lack of a complete symptom list may limit clinicians’ and school nurses’ ability to identify young people with depression early. Yet, little information is available that comprehensively describes depressive symptoms in this population, even though there have been many studies on depression in young people. Given the significance of understanding depressive symptoms manifested in young people, the aim of this study was to address this gap by conducting a scoping review and a content analysis to provide a comprehensive taxonomy of depressive symptoms or manifestations in young people. A taxonomy is a structured naming and classification scheme used to organize knowledge. The findings of this study will be of value to school nurses and clinicians to gain a better understanding of young people’s depressive symptoms.
This study used a scoping review method to synthesize the existing literature on depressive symptoms expressed in young people. Scoping reviews are appropriate to explore literature, map and summarize the evidence, and inform future research (Tricco et al., 2018). A five-step framework by Arksey and O’Malley (2005) was followed: (a) identifying the research question; (b) identifying relevant studies; (c) selecting studies; (d) charting the data; and (e) collating, summarizing, and reporting the results. In addition to Arksey and O’Malley’s framework, the recommendations of conducting scoping review provided by Levac et al. (2010) and Lockwood and Tricco (2020) were consulted in conducting this study.
The research question used to guide this scoping review study was “What depressive symptoms do young people experience that can be identified in peer-reviewed journal articles?”
Three electronic databases, MEDLINE, CINHAL, and PsycINFO, were searched for eligible peer-reviewed articles. The following search terms were used: depression, selfreported data, sign or symptom, adolescent, and young adults. These search terms were derived and extended from the National Library of Medicine’s Medical Subject Headings (MeSH), CINAHL Subject Headings, and synonyms. For example, key words and MeSH terms indicating young people, such as “adolescent,” “young adult,” and “youth,” were used in the search criteria to identify relevant peerreviewed journal articles. Studies were included if they applied narrative data collection approaches such as interviews, focus groups, or open-ended questions as part of the data collection procedure. The search strategies were discussed with a senior research librarian with extensive experience in scoping reviews. The final search strategy used in this study is included in Online Appendix A.
Depression and anxiety are often included together in research projects. In order to capture articles mainly focusing on depression, the term “anxiety” was set as an exclusion criterion. However, this decision does not diminish the wellknown association between depression and anxiety because the search strategies were applied only to titles, key words, standardized subject headings, and abstracts. Anxiety is one symptom commonly found in individuals with depression and would be captured in the symptom list. There were no restrictions on countries and publication dates; only papers with full texts published in English were included. Databases were searched from the inception of each database to March 2018 when this study was initiated. A second search was conducted to identify additional publications from March 2018 to March 2021.
After removal of duplicates, the articles were ready for initial screening by two nursing graduate student research assistants. One student reviewed the titles and abstracts of the search results to determine which articles met inclusion criteria. In the case where the research assistant expressed doubt regarding suitability for inclusion, a second person assessed the disputed article, with the final decision arrived at by discussion.
After the initial screening, two research assistants were trained and reviewed the remaining articles with full texts to ensure that the articles presented information on depressive symptoms described or reported by young people. Studies were included if the approach included qualitative data collection or open-ended questions related to the description of depressive symptoms. Articles were excluded if depression was measured only by structured instruments (e.g., PHQ, HADS) or without providing any information about depressive symptoms of young people. Discrepancies between the two reviewers were resolved by consensus and by the engagement of a third reviewer.
To describe the selected articles from the previous step, the name of the first author, year of publication, country of origin, the purpose of the study, research design (e.g., quantitative and/or qualitative), age of the study population, and sample size were charted. A content analysis was chosen to answer the research question of this study. Content analysis is a qualitative-synthesis type of analysis that combines qualitative approaches with quantitative analysis to categorize data (e.g., texts or images) to identify and code patterns in data (Barnett-Page & Thomas, 2009). Directed content analysis is guided by an existing theory, framework, or literature to construct an initial coding scheme for data analysis (Hsieh & Shannon, 2005). This approach was deemed a suitable analysis method for this study because existing literature was available for an initial coding scheme. This study had five dimensions in the initial coding scheme, including four depressive-symptom dimensions (i.e., cognitive, emotional, somatic, and interpersonal) proposed by Cheung and Power (2012) and a behavioral dimension. The behavioral dimension was used to capture manifestations such as aggressive behaviors and self-harm (Jackson et al., 2011; Osan & Chauhan, 2016; Twenge, 2020).
A codebook was established by building a coding scheme with five symptom dimensions. The selected articles were imported as pdf files into NVivo Version 11, a qualitative data analysis software. Two coders who were graduate nursing students were trained to use NVivo Version 11 and familiarized with the codebook; then, they coded a randomly selected 10% of the articles to calculate Cohen’s k coefficient for inter-rater reliability, a statistical approach to assess how well independent coders can agree with each other without any discussion. Cohen’s k greater than .80 is indicative of satisfactory reliability (Landis & Koch, 1977). Because the coders reached a satisfactory level of agreement, they coded the rest of the selected articles independently. The codes of symptoms within each dimension were created and consolidated in the coding process. Two coders met weekly to compare their coding results and the codebook content and discuss any coding issues with the principal investigator. For example, one research assistant coded “get mad” as “mad,” which was one symptom under the emotional dimension, while the other research assistant coded “get mad” as “anger”; after a discussion meeting, the code for this symptom was determined to be “anger.” This was an iterative process, and the list of symptoms in each dimension was continuously refined in NVivo Version 11 until no additional significant revisions were needed.
The findings were summarized and reported in accordance with the aim of this scoping review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was followed (Tricco et al., 2018) to enhance the overall completeness of reporting. A PRISMA diagram was used to document the numbers of search results retrieved (Tricco et al., 2018).
As this study was initially conducted in March 2018, an additional literature search was performed to retrieve eligible articles published between March 2018 and early March 9, 2021, using the same search and selection criteria mentioned above. This search and selection process was summarized in a PRISMA diagram. The additionally selected journal articles were used to verify the symptom taxonomy found in this study and evaluate whether additional codes or dimensions were needed. The process of verification was carried out by two coders with clinical nursing experience and trained to use NVivo Version 11. After the inter-rater reliability (i.e., Cohen’s k) was achieved with .80 or above, the coders coded the data independently by using the codebook developed, see “Charting the Data” section. Two coders met weekly to work together to reconcile any differences. The coding results were summarizedandcomparedbasedonthedepressivesymptom taxonomy established in this study.
The details pertaining to the selection of the sources of evidence can be found in Supplemental Figure S1, the PRISMA flowchart diagram, based on the methodology presented by Tricco et al. (2018). In the original search conducted in March 2018, the database searches identified 2,983 articles and a total of 17 peer-reviewed articles were selected for this scoping review.
The 17 selected articles dated from the year 1979 to 2014; this included four from 1979 to 2000 (Inamdar et al., 1979; Kazdin et al., 1985; Whitbeck et al., 2000; Yilmaz & Weiss, 2000), 11 from 2001 to 2010 (Angst et al., 2006; Chan & Poulin, 2009; Farmer, 2002; Fornos et al., 2005; Hankin et al., 2005; Hindin & Gultiano, 2006; Humensky et al., 2010; Lindsey et al., 2006; Slimmer, 2005; Wallin & Ahlström, 2005; Ybarra, 2004), and two from 2011 to 2014 (Martínez-Hernáez et al., 2014; Yuwen & Chen, 2013). The study populations were aged between 8 and 26 years. The majority of the articles were of U.S. origin (n = 11, 65%) and six articles from other countries. Most of the selected articles utilized a qualitative design (n = 16, 94%) as part of the study design. One case study exploring cultural formulation from a young immigrant was included. Results sections from the studies using the qualitative design or data collection approaches provided narrative data from study participants who were adolescents or young adults experiencing depression; the textual data contained useful symptom information for data analysis. Supplemental Table S1 provides a summary of the characteristics of the articles.
Three articles were randomly selected from the 17 articles to examine the inter-rater reliability for two coders; the results yielded an average Cohen’s k of .86, representing almost perfect inter-coder reliability (Viera & Garrett, 2005). Follow-up refinements to the codebook resulted in perfect (100%) agreement. The remaining articles were independently coded by two coders. A total of 60 symptoms were extracted and categorized into five symptom dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10; see Supplemental Table S2). The extracted symptoms were well captured by the symptom dimensions.
During the content analysis, the codes in the codebook were used more than 2,000 times because each code can be used more than one time in each article. To present the code(s) used in an article level, a code occurring two or more times within one article was counted as once; this limited the range of frequency of each code between 0 and 17, as there were 17 selected articles in this study. This resulted in a total of 338 code usages for all five symptom dimensions. All 17 articles include at least one symptom from the interpersonal dimension (Supplemental Table S2). In Supplemental Table S2, the number in the column of “Found in Articles” presents the individual symptom extracted in each article. For example, the code of “addictive behaviors” was extracted from 11 articles; if mentions of addictive behaviors were found three times in an article, it was counted once for that article.
The taxonomy of depressive symptoms in young people is shown in Supplemental Table S2, and the definition of each dimension is described in detail below. The behavioral dimension is defined as the manifestations of depression resulting in deliberate actions or observable consequences when engaging in society. Symptoms from this dimension differ from those in the interpersonal dimension when no target is identified or when the target is oneself. Eight symptoms in this dimension were coded in 14 selected articles; the most-coded symptoms were addictive behaviors, impaired performance, suicide attempts, deviant behaviors, and violence and aggression. The cognitive dimension is defined as a broad range of effects that impair thinking and beliefs, such as negative or disordered thinking about oneself or others, problems concentrating or delays in processing thoughts, distractibility, forgetfulness, impaired reactions, and indecisiveness. Sixteen selected articles were coded for 14 cognitive-related symptoms; the majority of symptoms were low self-esteem, suicidal ideation, hopelessness, difficulty concentrating, and loss of interest. The emotional dimension refers to the negative feelings associated with depression. Fifteen selected articles were coded for 15 emotional-related symptoms, and the majority of symptoms were sadness, shame, feeling anxious, and feeling irritable. The interpersonal dimension refers to a mutual interaction with a person or persons related to individuals’ depression. Of 13 interpersonal-related symptoms, the most frequently coded were negative interactions with others, being socially isolated, and lack of social support. The somatic dimension is defined as the physical manifestations of depression. Ten symptoms were identified for this dimension and coded in 15 selected articles. Loss of energy, changes in sleep, and crying were frequently coded in this dimension (Supplemental Table S2).
In the additional search conducted in March 2021, 460 articles published between 2018 and March 2021 were identified; a total of eight peer-reviewed articles were selected to verify the findings above (Achterbergh et al., 2020; Burke et al., 2018; Couture et al., 2020; Mazzuca et al., 2019; Morey-Nase et al., 2019; Reed-Berendt et al., 2019; Watson et al., 2020; Willis et al., 2018; Supplemental Figure S2). Seven articles used qualitative approaches and one applied qualitative meta-synthesis to explore the loneliness experience of young people with depression from 14 studies. The sample in these articles were aged between 11 and 26 years and were from United States (n = 3), United Kingdom (n = 2), India (n = 1), Zimbabwe (n = 1), and Australia (n = 1; Supplemental Table S1). No additional codes of symptom and symptom dimension emerged from these articles of the 60 symptoms; only one symptom, “mixing with the wrong crowd” from the Interpersonal dimension, was not used (Supplemental Table S2). This result indicated that the depressive symptom taxonomy was comprehensive and representative for describing depressive symptoms in young people.
This scoping review followed a suggested methodological framework (Arksey & O’Malley, 2005) and the PRISMA- ScR checklist (Tricco et al., 2018) to address a research question of identifying young people’s depressive symptoms. A total of 25 peer-reviewed articles were retrieved from three international scientific databases in the field of health sciences; 17 articles in the original search were used to generate the list or taxonomy of 60 depressive symptoms that manifest in young people, and eight articles were used to verify this taxonomy. These articles inclusively presented participants who were from a different culture or ethnic background (e.g., African American, Chinese American, and Mexican American), who were from the United States and other countries (e.g., Philippines, Canada, Switzerland, Sweden, Spain, Zimbabwe, India, Australia, and United Kingdom). Moreover, the sample of the articles were not only typical students but also those who may be seen by school nurses, such as young people who had a disease (e.g., cancer or AIDS), or who were homeless or refugees.
All of the selected articles contributed to answering the research question of this scoping review. Using a multidimensional depressive-symptoms model with five symptom dimensions in the directed content analysis, a comprehensive list or taxonomy of 60 depressive symptoms that manifest in young people was identified. The study captured the symptoms commonly reported by young people or individuals with depression, including addictive behaviors, sadness, low self-esteem, suicidal ideation, being socially isolated, loss of energy, self-neglect, hate, and difficulty making decisions (Bernaras et al., 2019; Lopez Molina et al., 2014; Moreh & O’Lawrence, 2016; O’Beaglaoich et al., 2020). Comparing to the depressive-symptom dimensions and symptoms from DSM-5 (American Psychiatric Association, 2013) and Cheung and Power (2012), the findings of the five symptom dimensions with 60 symptoms cover both common symptoms of depression (e.g., sad, changes in sleep, and changes in appetite or eating) in depression and symptoms that are known to be associated with young people depression. For example, suicide attempts, irritability, and anger are common symptoms among young people with depression (Jackson et al., 2011; Moreh & O’Lawrence, 2016; Walters et al., 2018); substance use or addictive behaviors are highly associated with depression in young people (Lim et al., 2016; Walters et al., 2018).
Despite such promising findings, any generalization needs to be made with caution. The articles used in this scoping review included samples from the United States and other countries. Although the cultural variations in the presentation of depression symptoms are out of the scope of this study, this taxonomy of depressive symptoms can be used to study the cultural difference in depression symptoms giving an understanding of depressive symptoms in different cultures provides useful information in the clinical assessment (Dreher et al., 2017). Moreover, the symptoms extracted in this study were conceptually grouped into their dimension by coders with a nursing background. Others may have different opinions about how to group symptoms. Therefore, interpretation of the symptom dimensions in this study needs to be made with caution. For example, changes in sleep and changes in appetite or eating were categorized into the somatic dimension in this study, whereas they were categorized into the behavioral dimension in another study (Osan & Chauhan, 2016). If researchers choose to use dimensions to summarize or interpret the findings of depressive symptoms, providing detailed information on how those symptoms were grouped into the symptom dimensions, as part of the background information, is suggested.
Depression is often associated with other psychiatric disorders, such as anxiety and post-traumatic stress disorder (PTSD), in young people (Espil et al., 2016; Walters et al., 2018). Some depressive symptoms are possibly concurrent with anxiety and PTSD; therefore, clinicians and researchers need to be aware that symptoms listed in the depressivesymptom taxonomy in this study that individuals manifested could also be a feature of other psychiatric disorders, such as anxiety, PTSD, and substance abuse disorder. For school nurses who plan to use this symptom taxonomy, referral for a further diagnosis is recommended for appropriate interventions or treatments.
As with all research, certain limitations are present. First, this study is limited by including only articles published in English; some relevant articles may have been missed. Second, this study focused on reporting relevant depressive symptoms in young people but did not focus on reporting the effect of the interventions on depression; therefore, this study did not use a standard appraisal tool to evaluate the study quality of each included article. However, this did not influence the findings of this study as the aim of this study was to identify young people’s depressive symptoms that were reported or discussed in the article and not to synthesize the effectiveness of any intervention outcomes. Moreover, the content analysis of this study analyzed selected articles, which were peer-reviewed articles from one of the three electronic literature databases. It is possible that relevant information may have been presented in appendices or supplements to the articles or in gray literature, which is not published through regular peer-reviewed processes. However, these limitations should not affect the findings of this study because this study met Nussbaumer-Streit et al.’s (2018) suggestion. In Nussbaumer-Streit et al.’s (2018) article, the authors suggested that a false conclusion based on the literature search can be minimized when two or more electronic literature databases are used in searching during the review process and 10 or more articles are reviewed. Because this study used three electronic literature databases and a total of 25 articles were reviewed, the findings of this study were presented with confidence.
Care in schools plays a critical role in promoting the health and safety of young people and helping them establish lifelong healthy behaviors (Centers for Disease Control and Prevention [CDC], 2016). Students having mental health conditions experience challenges in their academic achievement. Because poor mental health conditions can have long- term impacts, which can extend into adulthood, it is important to prevent or manage those conditions as early as possible.
School nurses are the key to the health of many students (National Association of School Nurses, 2020) and support the mission of the McKinney–Vento Act (U.S. Congress, 2015) by promoting the well-being of school-age children and youth experiencing homelessness or housing instability. They influence young people’s mental health by detecting and referring those who may be experiencing depression (Law et al., 2017; Mattey, 2019) and other mental health issues. Early identification allows for more effective intervention. Depression-assessment measures, such as PHQ-9 and BDI (Siu, 2016), are commonly used by school nurses but only cover a subset of symptoms (Cheung & Power, 2012). The taxonomy of depressive symptoms generated from this study can be used as a Supplemental Information source to help school nurses be aware of other depressive symptoms in addition to those measured in the depression-assessment measures. Moreover, the taxonomy of depressive symptoms in this study were generated from various populations, including samples from a minority group (Lindsey et al., 2006), different ethnic or cultural groups (Fornos et al., 2005; Hindin & Gultiano, 2006; Mazzuca et al., 2019; Willis et al., 2018; Yuwen & Chen, 2013), homeless (Whitbeck et al., 2000; Yilmaz & Weiss, 2000), and refugees (Wallin & Ahlström, 2005). The taxonomy of young people’s depressive symptoms from this study can help school nurses to identify young people suffering from symptoms of depression who are from these vulnerable groups. Finally, the five symptom dimensions of the taxonomy of depressive symptoms in this study provide an inclusive way to conceptualize depressive symptoms, so it can support school nurses to describe or summarize young people’s symptoms and behaviors.
The symptom taxonomy presented in this article can serve as a base for further research to enhance screening strategies for young people with depression. For example, knowing that interpersonal items are underrepresented in depression-screening instruments (Cheung & Power, 2012), the interpersonal dimension aspects identified from this study, such as being bullied, which are more specific to young people’s symptoms, can be used for refining or expanding a depression-assessment instrument. Moreover, instead of only asking young people a few questions from an assessment instrument, the young people depressive-symptom taxonomy from this study can be used to support research studies aimed at detecting depressive symptoms from young people’s social media communications or texts (Jung et al., 2017; Mullarkey et al., 2019).
Promoting mental health in young people can benefit society in the long term by developing behaviors that lead to healthy and productive adults (Bjørnsen et al., 2019). School nurses are in a prime position to screen young people who may experience depression. The comprehensive taxonomy of symptoms generated from this review can be used for improving depression prevention and treatment by supporting school nurses in the early identification of depression in young people. Furthermore, this symptom taxonomy offers insight into the experiences of young people with depression by extracting the narrative data described or reported by the sample with depression. Finally, this taxonomy of depressive symptoms is important to school nurses, clinicians, and researchers because it can supplement the existing screening measures of depression and provides a better understanding of representative symptoms in this vulnerable population.
Ethical approval was not required for this scoping review synthesizing published literature.
We would like to thank the research assistants for the project: Djin Tay, Sindy (Yin-Shun) Chiu, and Sehee Jung in the original search stage of the study and Ching-Yu Wang in the additional search stage of the study.
We acknowledge that all authors listed meet the authorship criteria according to the guidelines of the International Committee of Medical Journal Editors (ICMJE). Jia-Wen Guo, Mike Conway, and Wendy W. Chapman contributed to the conception and design of the study. Jia-Wen Guo, Brooks R. Keeshin, and Katherine A. Sward contributed to acquisition, analysis, and interpretation. Jia-Wen Guo drafted the manuscript and agrees to be accountable for all aspects of work ensuring integrity and accuracy. All authors critically revised the manuscript and gave final approval.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Intermountain Foundation at Primary Children’s Hospital Early Career Development Award (principal investigator: Guo).
Jia-Wen Guo, PhD, RN https://orcid.org/0000-0002-4698-4696
Supplemental material for this article is available online.
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Jia-Wen Guo, PhD, RN, is an Associate Professor at the College of Nursing, University of Utah.
Brooks R. Keeshin, MD, is an Associate Professor at the Department of Pediatrics, University of Utah.
Mike Conway, PhD, is an Assistant Professor at the Department of Biomedical Informatics, University of Utah.
Wendy W. Chapman, PhD, is a Professor at The Centre for Digital Transformation of Health, University of Melbourne.
Katherine A. Sward, PhD, RN, FAAN, is a Professor at the College of Nursing, University of Utah.
1 College of Nursing, University of Utah, Salt Lake City, UT, USA
2 Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
3 Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
4 The Centre for Digital Transformation of Health, University of Melbourne, Victoria, Australia
Corresponding Author:Jia-Wen Guo, PhD, RN, College of Nursing, University of Utah, 10 South 2000 East, Salt Lake City, UT 84112, USA.Email: jia-wen.guo@nurs.utah.edu
