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Research Paper
Ten-year trends in research designs and keywords: a bibliometric comparison of the Journal of Korean Academy of Nursing and leading international nursing journals
Jin-Hee Park1,*orcid, Hyun Kyoung Kim2,*orcid, Gaeun Kim3orcid, Sun Hyoung Bae1orcid

DOI: https://doi.org/10.4040/jkan.25119
Published online: November 19, 2025

1College of Nursing · Research Institute of Nursing Science, Ajou University, Suwon, Korea

2College of Nursing, Kongju National University, Gongju, Korea

3College of Nursing, Keimyung University, Daegu, Korea

Corresponding author: Sun Hyoung Bae College of Nursing, Ajou University, 164 World cup-ro, Yeongtong-gu, Suwon 16499, Korea E-mail: shyoung@ajou.ac.kr
*These authors contributed equally as co-first authors.
• Received: August 22, 2025   • Revised: September 30, 2025   • Accepted: September 30, 2025

© 2025 Korean Society of Nursing Science

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License (http://creativecommons.org/licenses/by-nd/4.0) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

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  • Purpose
    This study compared trends in research designs and keywords by analyzing the abstracts of four major nursing journals over the past decade, focusing on the Journal of Korean Academy of Nursing (JKAN) in comparison with the International Journal of Nursing Studies (IJNS), Journal of Advanced Nursing (JAN), and Japan Journal of Nursing Science (JJNS).
  • Methods
    A bibliometric analysis was conducted, encompassing 5,522 abstracts published between 2015 and 2024. Research designs were first classified as “quantitative,” “qualitative,” or “other,” and then further sub-classified based on international evidence-based frameworks. Text preprocessing was also conducted, and term frequency–inverse document frequency was applied to evaluate keyword importance. The 2015–2019 and 2020–2024 periods were compared to examine changes in both research designs and keyword importance.
  • Results
    Compared to IJNS, JAN, and JJNS, JKAN published more instrument development and analytic studies but fewer randomized controlled trials and systematic reviews. Over time, the number of instrument development and mixed-methods studies in JKAN increased, while high-evidence designs remained scarce. Keyword analysis showed JKAN’s emphasis on psychosocial themes such as self-efficacy, quality of life, and depression, whereas the other journals more often highlighted policy- and institution-related topics. Across journals, COVID-19 and patient safety emerged as important themes after 2020.
  • Conclusion
    JKAN demonstrates strengths in methodological diversity within quantitative research and in digital health–related analytics. However, high-evidence study designs and policy-oriented keywords are underrepresented in JKAN. Strategic expansion toward randomized controlled trials, systematic review, global and digital health, and policy-relevant research is recommended to strengthen JKAN’s international competitiveness.
  • Key words: Bibliometrics; Data mining; Nursing research; Research design; Trends
Globally, healthcare systems are confronting unprecedented challenges due to the increasing severity of complex health problems, such as aging, chronic disease, and infectious disease pandemics. These growing issues highlight the importance of empirical research and multidisciplinary perspectives, including treatment-based approaches as well as prevention, health management, and social determinants of health [1]. Concurrently, rapid advancements in Industry 4.0 and the digitalization of healthcare have led to advances in the application of digital health, big data, and artificial intelligence (AI)-based technology in nursing research and practical settings [2]. These advances have led to important shifts in the structure and direction of nursing knowledge production [3,4]. Alongside accelerated globalization in nursing research, there has been clear diversification of research topics and refinement of methodologies, particularly in Science Citation Index Expanded (SCIE) international nursing journals [3-6].
The Journal of Korean Academy of Nursing (JKAN) is a SCIE journal with a long tradition and academic authority in the field of nursing in South Korea; it has played a central role in the growth and development of nursing in South Korea over the last several decades [7]. Recently, multidimensional efforts have been made to improve the quality and international standing of the journal, including efforts to reinforce research ethics, apply rigorous statistical analyses and internationalize the online submission system [4]. However, to further enhance the academic status and global competitiveness of JKAN in the context of the rapid globalization of nursing research, a strategic review of the current international academic environment, including collaboration between researchers, methodology refinement, social contributions, and clinical applicability, is needed. Such a review is expected to identify directions for JKAN development [4,8].
Various quantitative analysis techniques have recently been introduced to nursing, including text mining, network analysis, machine learning, and structural equation modeling, making quantitative analyses of large-scale text data feasible [9,10]. Text mining is a quantitative analytical tool for extracting topics and themes from large-scale academic text data and structuring the relationships between them. This approach is widely used to explore the structure and changes in academic discourse [3,5]. However, few empirical analyses have focused on the application of these analytic techniques at the level of nursing journals published in Korea and their position in relation to major international journals [3].
The purpose of this study was to compare trends in research designs and keywords based on abstracts published in major nursing journals over the last decade. In addition to JKAN, the International Journal of Nursing Studies (IJNS), Journal of Advanced Nursing (JAN), and Japan Journal of Nursing Science (JJNS), which have maintained positions near the top of the SCIE ranking, were included in the analyses. In particular, through a quantitative analysis of changes over the past 10 years, we aimed to obtain a broad view of research trends and the status of JKAN in the context of international research. We expect our findings to be used to explore strategic directions for improving the standing of JKAN as an international journal.
1. Research design
This study conducted a bibliometric analysis to examine and compare trends in research designs and keywords, using abstracts of articles published between 2015 and 2024 in JKAN and three leading SCIE nursing journals (IJNS, JAN, JJNS).
2. Data collection and preparation
The abstracts of scholarly articles published from 2015 to 2024 in four major nursing journals, namely JKAN, IJNS, JAN, and JJNS, were analyzed. The selection of target journals was based on several criteria. We prioritized general nursing journals indexed in the SCIE and ranked in Q1 or Q2 according to bibliometric indicators (e.g., H-index). Journals with a narrow focus on specific clinical skills or specialized domains were excluded in favor of those covering a broad spectrum of nursing topics. The final selection of the four journals was determined through a consensus process involving six PhD-prepared nursing researchers.
Abstracts were retrieved from the official journal websites and the PubMed database. The collected data were organized using EndNote software (Clarivate) and then exported to Microsoft Excel (Microsoft Corp.) for management. An initial total of 6,853 abstracts were retrieved. Of these, 1,155 records were excluded during the initial screening because they were not empirical research articles (e.g., editorials, corrections, and conference abstracts). An additional 176 records were removed due to the absence of an abstract or keywords. Finally, 5,522 abstracts were included in the analysis.
3. Text preprocessing
To prepare the abstract data for a keyword analysis using text mining techniques, text preprocessing was conducted in a Python-based Jupyter Notebook environment [11]. First, all keywords were converted to lowercase, and extraneous characters—such as parentheses, special symbols, and numbers—were removed to ensure uniformity. Synonymous terms were standardized by referencing the Medical Subject Headings (MeSH) thesaurus. For instance, the terms self-care, self_care, and self_management were normalized to self-care, while elderly, older adults, and aged people were unified as aged.
To preserve the semantic integrity of compound phrases, multi-word expressions, such as mental health, are connected with an underscore and treated as a single term (i.e., mental_health). Lemmatization was applied to reduce words to their base or root form (e.g., studies → study and interventions → intervention). Furthermore, semantically related terms, such as nurse and nursing, were consolidated into a single term (e.g., nurse).
Terms describing research design types, such as randomized controlled trial and randomized clinical trial, were also standardized (e.g., randomized_controlled_trial). A total of 20 key research design types were identified and consistently labeled. Finally, both general and domain-specific stopwords that offered minimal semantic value (e.g., effect, result, sample, and published) were removed to improve analytical precision.
Following preprocessing, a document-term matrix was constructed, and term frequency–inverse document frequency (TF-IDF) weights were calculated for each term. These weighted values formed the basis for identifying keyword importance and analyzing temporal trends.
4. Data analysis
All analyses were performed using Python ver. 3.10 (https://www.python.org/) in a Jupyter Notebook environment. The pandas library was used for data manipulation, matplotlib and seaborn libraries were employed for visualization, and scikit-learn was used for text mining operations.
Research design classification was guided by established international frameworks, including the National Institute for Health and Care Excellence [12] public health guideline methodology, Duke University Medical Center Library’s typology of review types [13], Oxford Centre for Evidence-Based Medicine’s research design glossary [14], and Standards for Reporting Qualitative Research (SRQR) [15]. Based on these references and through consensus among the research team, each article was classified into one of three major categories: quantitative, qualitative, or other.
Quantitative studies were further subdivided into experimental, observational, secondary data analysis, instrument development and validation, review, and special analytic studies. Qualitative studies were categorized into two groups: (1) traditional approaches grounded in explicit philosophical frameworks (e.g., phenomenology, grounded theory, ethnography, narrative inquiry, and case study) and (2) other approaches that, while not explicitly grounded in a particular philosophy, relied on distinct analytic techniques (e.g., qualitative descriptive studies, Q-methodology, content analysis, and thematic analysis) (Table 1). The frequency of each research design was calculated to determine the distribution across journals. The relative proportion of each design was also analyzed annually from 2015 to 2024. To assess temporal shifts, the study period was divided into the 2015–2019 and 2020–2024 periods, and the proportions of design types were compared between these two periods.
For the keyword analysis, TF-IDF scores were calculated for each year to assess the relative importance of keywords over time. The average TF-IDF score for each keyword across the entire decade (2015–2024) was then computed, and the top 20 keywords with the highest average scores were identified. The analysis period was further divided into the 2015–2019 and 2020–2024 periods. The top 15 keywords based on average TF-IDF scores were identified for each period. A comparison between the periods was conducted to identify persistent, emerging, and declining keywords, thereby revealing shifts in research focus over time.
5. Ethical considerations
This study was a bibliometric analysis based on existing literature and did not involve any human participants or identifiable personal data. Therefore, it was exempt from review by the Institutional Review Board.
1. Research design types by journal
In total, 5,522 articles published between 2015 and 2024 in four target nursing journals (JKAN, IJNS, JAN, and JJNS) were analyzed. Overall, quantitative studies were the most prevalent, accounting for 76.0% of the articles, followed by qualitative studies (17.0%) and mixed-methods studies (4.6%) (Table 1).
JKAN exhibited the highest proportion of quantitative studies (85.4%), primarily comprising cross-sectional surveys (32.5%) and non-randomized controlled trials (non-RCTs) (22.8%). These were followed by instrument development and validation studies (14.7%) and systematic review (SR) and meta-analysis (6.8%). Qualitative studies accounted for 11.9% of articles in JKAN, with a majority (70.3%) employing traditional qualitative approaches.
IJNS similarly featured a high percentage of quantitative studies (86.1%). SR and meta-analysis were the most common designs (36.4%), followed by cross-sectional surveys (18.1%) and randomized controlled trials (RCTs) (13.3%). Qualitative studies represented 9.1% of publications, with 54.5% using other qualitative approaches.
JAN had a lower proportion of quantitative studies (67.4%) than IJNS, with cross-sectional surveys (35.6%), SR and meta-analysis (19.6%), and RCTs (7.6%) being the predominant designs. Qualitative studies accounted for 23.0% of its articles—the highest proportion among the four journals—with 53.7% of these using other qualitative approaches. Mixed-methods studies were also relatively common in JAN (5.9%).
JJNS reported the highest proportion of quantitative studies (86.9%). The most frequent designs were cross-sectional surveys (46.9%), non-RCTs (14.9%), and RCTs (11.6%). Qualitative studies accounted for 11.0% of its articles, of which 57.9% utilized other qualitative approaches (Table 1).
2. Changes in research designs by journal
To examine temporal changes in research designs, the analysis period was divided into the 2015–2019 and 2020–2024 periods. The relative proportions of major research designs were then compared for each journal (Figure 1). While the ranking of the top research designs remained generally similar across all four journals, their proportions shifted between the two periods.
In JKAN, the proportion of non-RCTs decreased from 22.8% during the 2015–2019 period to 14.6% in the 2020–2024 period. In contrast, instrument development and validation studies increased from 11.4% to 14.2%. Other qualitative approaches (2.4% → 5.1%) and mixed-methods studies (1.6% → 3.6%) also increased (Figure 1A).
In IJNS, SR and meta-analysis showed a high relative proportion in both periods, with a substantial increase from 23.5% to 37.0%. Conversely, the proportions of cross-sectional surveys (19.4% → 12.8%), other reviews (13.2% → 11.7%), and RCTs (11.9% → 11.2%) all decreased. Similarly, qualitative and mixed-methods studies showed a slight decline in the 2020-2024 period compared with the 2015-2019 period (Figure 1B).
In JAN, the proportions of cross-sectional surveys (25.7% → 22.8%), RCTs (6.3% → 4.2%), and non-RCTs (3.8% → 2.8%) decreased. In contrast, SR and meta-analysis (11.3% → 14.5%), other reviews (5.9% → 9.0%), and secondary data analyses (2.3% → 4.6%) all increased. Other qualitative approaches rose from 9.7% to 14.3%, while mixed-methods research remained stable at approximately 6.0% in both periods (Figure 1C).
In JJNS, cross-sectional surveys increased substantially from 38.4% to 42.1%. RCTs (8.9% → 10.7%) and SR and meta-analysis (2.6% → 5.2%) also increased. Conversely, instrument development and validation studies (12.1% → 4.0%) and non-RCTs (15.3% → 11.6%) decreased. Traditional qualitative approaches increased from 3.7% to 5.2%, while other qualitative approaches decreased slightly from 6.8% to 6.1% (Figure 1D).
3. Analysis of relative keyword importance by journal
An analysis of the top 20 keywords with the highest average TF-IDF scores from 2015 to 2024 revealed that ‘nurse,’ ‘aged,’ and ‘self-care’ were common high-importance keywords across all four journals (Table 2).
In JKAN, ‘nurse’ demonstrated the highest importance (average TF-IDF=.251). The term ‘covid19’ ranked second (average=.133). Keywords related to psychosocial factors, including ‘self-efficacy’ (.115), ‘aged’ (.108), ‘qol’ (.105), and ‘depression’ (.100), were also highly ranked. In addition, terms associated with measurement and reproducibility, such as ‘validity’ (.084), ‘statistical factor analysis’ (.078), and ‘reproducibility of result’ (.069), were prominent.
In IJNS, top keywords included ‘nurse’ (.242), ‘aged’ (.157), ‘self-care’ (.133), ‘dementia’ (.120), and ‘hospital’ (.103). Psychosocial factors, such as ‘depression’ (.078), ‘anxiety’ (.048), and ‘self-efficacy’ (.049), were also notable. Furthermore, terms related to long term care and safety, including ‘nursing home’ (.080), ‘long term care’ (.054), and ‘patient safety’ (.065), were distinctive features of IJNS.
In JAN, ‘nurse’ had the highest average TF-IDF score among the four journals (.553), followed by keywords related to the life course, maternity, and infectious disease response, such as ‘aged’ (.125), ‘self-care’ (.101), ‘midwife’ (.100), and ‘covid19’ (.075). Measurement-related terms and psychological factors (‘instrument development,’ ‘qol,’ and ‘self-efficacy’) were also frequent, along with keywords in education and mental health (‘nursing student,’ ‘nursing education,’ ‘mental health,’ and ‘burnout’).
In JJNS, ‘covid19’ ranked highest (average TF-IDF=.135). Other highly ranked keywords included ‘nurse’ (.125), ‘aged’ (.095), ‘self-care’ (.076), and ‘qol’ (.075). Psychological factors, such as ‘depression’ (.054), ‘anxiety’ (.050), and ‘social support’ (.047), were also prominent. In addition, terms related to education and measurement, including ‘health education’ (.045), ‘reliability’ (.043), and ‘validity’ (.048), were identified.
4. Changes in keyword importance over time by journal
To investigate shifts in keyword importance, we calculated the average TF-IDF scores for the 2015–2019 and 2020–2024 periods. The top 15 keywords from each period were extracted and compared using bidirectional bar charts (Figure 2).
In JKAN, ‘nurse,’ ‘self-efficacy,’ ‘adolescent,’ ‘aged,’ ‘health behavior,’ ‘qol,’ ‘critical care,’ ‘validity,’ and ‘depression’ were prominent keywords in both periods. The importance of ‘nurse’ increased (from .038 to .054), ranking highest in both periods. In contrast, the relative importance of ‘depression’ (.027 → .014) and ‘self-care’ (.020 → .013) decreased. In the 2020–2024 period, ‘statistical factor analysis,’ ‘reproducibility of result,’ and ‘covid19’ emerged as new high-ranking keywords (Figure 2A).
In IJNS, ‘nurse,’ ‘self-care,’ ‘aged,’ ‘dementia,’ ‘critical care,’ ‘depression,’ ‘hospital,’ ‘nursing home,’ and ‘patient safety’ remained important across both periods. However, the importance of ‘communication’ (.009 → .004), ‘burnout’ (.009 → .005), ‘pain’ (.008 → .007), and ‘long term_care’ (.008 → .007) declined in the 2020–2024 period. In contrast, ‘qol,’ and ‘palliative care’ emerged as new keywords in the 2020–2024 period (Figure 2B).
In JAN, the keyword composition showed little change between periods. Although the score for ‘nurse’ decreased slightly (.045 → .042), it remained the top keyword in both periods. The importance of ‘nursing education’ (.008 → .006) and ‘burnout’ (.009 → .007) declined in the 2020–2024 period, while ‘patient safety’ (.005 → .008) and ‘advanced practice nursing’ (.006 → .007) saw their TF-IDF scores rise and newly enter the top rankings (Figure 2C).
In JJNS, ‘nurse,’ ‘aged,’ ‘self-care,’ ‘qol,’ ‘critical care,’ ‘depression,’ ‘nursing student,’ and ‘nursing education’ were identified as top 15 keywords in both periods. The importance of ‘self-efficacy,’ ‘instrument development,’ and ‘burnout’ declined in the 2020-2024 period, while ‘anxiety,’ ‘health education,’ and ‘validity’ emerged as new high-ranking keywords (Figure 2D).
We analyzed trends in the types of research designs and major keywords in nursing journals over the last 10 years in a comparative framework, with the aim of examining the current academic standing of JKAN and suggesting directions for future development. Academic leadership contributes to the link between clinical practice and research and is formed through high-quality education and professional development [16]. Within this context, the role of JKAN in advancing such leadership assumes considerable significance. The two axes analyzed in this study—research designs and keywords—could serve as important indices for identifying future directions in nursing research.
In the last 10 years, all four journals showed clear changes over time in the relative frequencies of various research designs. While JKAN maintained a consistent composition with an overall focus on quantitative research, during the 2020–2024 period, there were steady increases in the ratios of mixed-methods and instrument development and validation studies. Specifically, the ratio of mixed-methods studies increased from 1.6% to 3.6%, and the ratio of instrument development and validation studies increased from 11.4% to 14.2%. The ratio of other qualitative approaches rose from 2.4% to 5.1%. These findings reveal a shift in JKAN, previously focused on cross-sectional studies, toward the inclusion of more diverse research designs.
There were several differences between JKAN and the IJNS, JAN, and JJNS, reflecting several limitations of the journal. For the IJNS, during the same period, there was a large increase in the ratio of SR and meta-analysis from 23.5% to 37.0%, the ratio of RCTs was maintained above 11%, and there was a stable stream of high-standard evidence-based research being published. The JAN showed gradual increases in the ratios of secondary data analysis and SR and meta-analysis, while the JJNS showed increasing ratios of both RCTs and SR and meta-analysis in the 2020–2024 period. JKAN remained focused on non-RCTs studies and cross-sectional studies, and the ratios of RCTs and SR and meta-analysis remained low or stagnant. These trends suggest that JKAN is still skewed toward single-center cross-sectional studies. For JKAN to position itself as a top international journal, the diversification of research designs, which can generate high-quality results, is essential. In particular, designs such as SR, meta-analysis, RCTs, and secondary data analyses can improve applicability in nursing and policymaking; these designs should be actively encouraged at the level of editorial strategy.
Qualitative research accounted for 11.9% of all studies published in JKAN, of which the majority (i.e., 70.3%) used traditional qualitative approaches. This pattern differed markedly from that of other journals, which showed a clear preference for grounded theory and phenomenological studies. By contrast, qualitative research represented 9.1%, 23.0%, and 11.0% of all studies in IJNS, JAN, and JJNS, respectively; in each of these journals, more than half of qualitative studies used other qualitative approaches. These journals were expanding their research designs, focusing on empirical and applicable themes, such as patient experiences, nursing work, and policy appraisal. For example, in JAN, 53.7% of qualitative studies—and in IJNS, 54.5%—used other qualitative approaches, such as qualitative descriptive studies or based on content and thematic analyses. This suggests that, even though JKAN ensures the depth and theoretical basis of qualitative studies, the scope is somewhat narrow with regard to field-adjacent themes or practical applications. In international nursing, qualitative and mixed-methods studies have recently been applied to complex issues, such as infectious diseases, nurse staffing crises, digital healthcare, and health inequity.
Studies using analytic techniques, such as data mining, machine learning, or AI, accounted for a low percentage of all studies in JKAN, at 2.3%. However, this was still higher than the ratios in the other three journals. This suggests that a strength of South Korean research is in the fields of digital health and nursing informatics, and these techniques could be a basis for expansion to more refined analytical research, such as big data-based empirical studies and predictive model development. Digital research has been a key area in nursing, including digital literacy and the development of mobile health applications; personalized digital interventions are also being attempted [17]. Going forward, JKAN could be a pioneer in the digital transformation of nursing through reinforcing nursing leadership based on the World Health Organization’s digital health strategy (2020–2025) and providing education in digital ethics [1].
Splitting the data into the two time periods of 2015–2019 and 2020–2024, we analyzed changes in the importance of keywords in major nursing journals. We demonstrated the shift in research keywords with changing social and public healthcare environments. First, patient-centric keywords, such as ‘self-care’ and ‘critical care’ showed consistently high rankings in all four journals. These results suggest that nursing research is focused on patients’ experiences and direct nursing behaviors, which are the essence of clinical nursing. In JKAN, in particular, the high importance of psychosocial keywords, such as ‘qol,’ ‘depression,’ and ‘self-efficacy,’ was maintained over a long period of time. The continued accumulation of research in these fields could be interpreted as a strength of JKAN.
Since the pandemic, ‘covid-19’ has shown a rapid increase in TF-IDF in all journals, emerging as a core keyword reflecting the current times. This is consistent with trends in global nursing research reported by Zhang et al. [10], who reported a shift in interest from ‘nursing,’ ‘burnout,’ and ‘fear’ during the pandemic to ‘stress,’ ‘depression,’ ‘nursing student,’ and ‘public health.’ Keywords in JKAN also reflected a focus on changes in the psychological health of nurses and nursing education during the pandemic, with terms such as ‘covid19,’ ‘depression,’ and ‘nurse,’ showing that the journal reacts promptly to time-sensitive social and public healthcare issues.
Among the nurse- and professional-related keywords, there was an increase in the importance of ‘nurse’ in all international journals. Themes related to nursing institutions and clinical systems, such as ‘nursing home,’ ‘patient safety,’ ‘long-term care,’ and ‘advanced practice nursing’ consistently occupied top positions in the IJNS and JAN. In JKAN, other than ‘nurse,’ relatively few keywords were directly related to nursing policies and institutions. These findings suggest that work-based research in nursing in South Korea could be improved in terms of policy connectedness. In the future, research including the expansion of the roles of nursing professionals, policy proposals, and improvements in organizational culture needs to be discussed actively through JKAN; this could allow JKAN to act as a platform for promoting knowledge production and facilitating communication between the policy and work sectors.
In terms of education-related keywords, performance-based psychology and competencies, such as ‘self-efficacy,’ ‘nursing student,’ ‘nursing education,’ and ‘health education,’ have been consistently highly represented in international journals. In JKAN, only the ‘student’ keyword showed a small increasing pattern in the 2020–2024 period. This demonstrates the relatively limited scope of keywords in JKAN related to education research. The education research currently published in JKAN is mainly focused on behavioral and psychological factors. A more structured/institutional approach is required to improve educator competencies, develop education programs, and increase the quality of nursing education.
In the list of top keywords in the TF-IDF analysis, there were almost no keywords related to global public healthcare issues, such as digital healthcare, climate change, and health equity. Several nursing journals, including JKAN, have not yet established these themes as central research areas. There is growing awareness of the need to conduct multidisciplinary research, including the digital transition and the response of international nursing policy. In the future, JKAN should accept more future-oriented keywords, such as ‘digital health,’ ‘global healthcare,’ ‘health inequity,’ and ‘nursing informatics,’ to adopt a strategic role as a research platform that links nursing work, policy, and education.
Our study had some limitations. First, although we analyzed the frequency and TF-IDF of keywords, focusing on studies published in four major nursing journals, the scope of journals included in the study was restricted. Therefore, the findings may not comprehensively reflect research trends across the field of nursing. Second, the metadata used in the thematic analysis were dependent on keyword data presented on the web page for each study; therefore, there is a possibility that actual research keywords were not thoroughly represented. Third, although the TF-IDF analysis is useful for quantitatively evaluating the importance of words, it cannot reflect the semantic context, relatedness, or structural relationships between keywords, limiting analyses of the multidimensional structure of research keywords. Fourth, in the analysis by year, we divided the 10-year period into the 2015–2019 and 2020–2024 periods. However, the disparity in the number of studies published between the 2015–2019 period (n=2,374) and the 2020–2024 period (n=3,148) could have affected the results. Fifth, after COVID-19, certain keywords temporarily became more prominent, and this phenomenon could have distorted the overall flow of research themes. Sixth, this study focused on quantitative analyses, and we were unable to perform qualitative analyses for an in-depth understanding of how each keyword in each study was actually used in context (i.e., information had to be inferred).
JKAN showed relatively low ratios of RCTs, SR, and meta-analysis, whereas instrument development and validation studies and data-based analytical studies were more prevalent than in other journals. This reflects the specific strengths of nursing in South Korea but also highlights the need to expand studies with higher levels of evidence (e.g., RCTs, SR, and big data-based research). International journals have steadily increased the representation of such designs, underscoring the need for similar strategic expansion in JKAN.
Keyword analysis revealed that other international journals addressed a wider scope of keywords—linking clinical practice, professional roles, healthcare systems, and education—while JKAN tended to concentrate on a limited range of psychological competency keywords, such as ‘nurse,’ ‘self-efficacy,’ and ‘nursing student.’ This indicates a relative lack of institutional or policy-linked perspectives. Expanding research to encompass broader professional roles, policy development, and organizational culture could strengthen the journal’s relevance and impact.
Themes related to the future healthcare environment, including digital technology, global nursing, and environmental issues, remain underrepresented across all journals. Actively incorporating these future-oriented themes could enable JKAN to respond to evolving healthcare demands, advance nursing education, and contribute to digital health and nursing systems, thereby enhancing the quality and international competitiveness of its research.

Conflicts of Interest

All authors are members of the editorial board of the Journal of Korean Academy of Nursing. However, they were not involved in the editorial handling, peer review, or decision-making process for this manuscript. The authors declare no other conflicts of interest, financial or personal, that could inappropriately influence or be perceived to influence this work.

Acknowledgements

None.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Sharing Statement

The data that support the findings of this study are available on reasonable request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Author Contributions

Conceptualization and/or Methodology: JHP, HKK, GEK, SHB. Data curation and/or Analysis: JHP, HKK, SHB. Funding acquisition: none. Investigation: JHP, HKK, GEK, SHB. Project administration and/or Supervision: JHP. Resources and/or Software: none. Validation: JHP, HKK, GEK, SHB. Visualization: JHP, SHB. Writing—original draft and/or Review & Editing: JHP, HKK, SHB. Final approval of the manuscript: all authors.

Fig. 1.
Changes in the distribution of study design types in the four major nursing journals, comparing the 2015–2019 and 2020–2024 periods. (A) Journal of Korean Academy of Nursing (JKAN): Study designs comparison (2015–2019 vs. 2020–2024). (B) International Journal of Nursing Studies (IJNS): Study designs comparison (2015–2019 vs. 2020–2024). (C) Journal of Advanced Nursing (JAN): Study designs comparison (2015–2019 vs. 2020–2024). (D) Japan Journal of Nursing Science (JJNS): Study designs comparison (2015–2019 vs. 2020–2024). RCT, randomized controlled trial.
jkan-25119f1.jpg
Fig. 2.
Changes in mean term frequency–inverse document frequency (TF-IDF) scores of study keywords across the four major nursing journals, comparing the 2015–2019 and 2020–2024 periods. (A) Journal of Korean Academy of Nursing (JKAN): Study topics comparison (2015–2019 vs. 2020–2024). (B) International Journal of Nursing Studies (IJNS): Study topics comparison (2015–2019 vs. 2020–2024). (C) Journal of Advanced Nursing (JAN): Study topics comparison (2015–2019 vs. 2020–2024). (D) Japan Journal of Nursing Science (JJNS): Study topics comparison (2015–2019 vs. 2020–2024).
jkan-25119f2.jpg
Table 1.
Distribution of research designs and methodologies in four leading nursing journals between 2015 and 2024 (N=5,522)
Category JKAN (n=621) IJNS (n=1,457) JAN (n=2,926) JJNS (n=518) Total (N=5,522)
Quantitative research 530 (85.4) 1,254 (86.1) 1,970 (67.4) 450 (86.9) 4,204 (76.0)
 Experimental study
  RCTs 25 (4.7) 167 (13.3) 149 (7.6) 52 (11.6) 393 (9.3)
  Non-RCTs 121 (22.8) 38 (3.0) 93 (4.7) 67 (14.9) 319 (7.6)
 Observational study
  Cross-sectional survey 172 (32.5) 227 (18.1) 702 (35.6) 211 (46.9) 1,312 (31.2)
  Longitudinal survey 21 (4.1) 85 (6.8) 130 (6.6) 29 (6.4) 265 (6.3)
 Review
  Systematic review 6 (1.0) 243 (19.4) 231 (11.7) 11 (2.4) 491 (11.7)
  Meta-analysis 31 (5.8) 213 (17.0) 156 (7.9) 11 (2.4) 411 (9.8)
  Other review (e.g., scoping review, integrative review, literature review, etc.) 14 (2.6) 180 (14.4) 226 (11.5) 13 (2.9) 433 (10.3)
 Methodological study
  Instrument development and validation 78 (14.7) 23 (1.8) 103 (5.3) 36 (8.1) 240 (5.8)
  Concept analysis 10 (1.9) 7 (0.6) 51 (2.5) 3 (0.7) 71 (1.6)
  Other methodology (e.g., diagnostic accuracy study, predictive model validation, intervention tool/protocol development, etc.) 3 (0.6) 0 (0.0) 9 (0.5) 0 (0.0) 12 (0.3)
  Secondary data analysis 33 (6.2) 64 (5.1) 107 (5.4) 14 (3.1) 218 (5.2)
 Special analyses
  Data mining and AI (e.g., text mining, network analysis, machine learning, judgment analysis, etc.) 12 (2.3) 1 (0.1) 7 (0.4) 2 (0.4) 22 (0.5)
  Other special analyses (e.g., cost-benefit analysis, cost-utility analysis, etc.) 4 (0.8) 6 (0.4) 6 (0.3) 1 (0.2) 17 (0.4)
Qualitative research 74 (11.9) 132 (9.1) 673 (23.0) 57 (11.0) 936 (17.0)
 Phenomenology 31 (41.9) 15 (11.4) 137 (20.4) 8 (14.0) 191 (20.4)
 Grounded theory 20 (27.0) 23 (17.4) 88 (13.1) 12 (21.1) 143 (15.3)
 Ethnography 1 (1.4) 8 (6.1) 43 (6.4) 0 (0.0) 52 (5.6)
 Narrative research 0 (0.0) 4 (3.0) 13 (1.9) 2 (3.5) 19 (2.0)
 Case study 0 (0.0) 10 (7.6) 30 (4.5) 2 (3.5) 42 (4.5)
 Other qualitative approach (e.g., qualitative descriptive, Q-methodology, content/thematic analysis, etc.) 22 (29.7) 72 (54.5) 362 (53.7) 33 (57.9) 489 (52.2)
Mixed-methods research 15 (2.4) 53 (3.6) 174 (5.9) 10 (1.9) 252 (4.6)
Other research (discursive paper) 2 (0.3) 18 (1.2) 109 (3.7) 1 (0.2) 130 (2.4)

Values are presented as number (%).

AI, artificial intelligence; IJNS, International Journal of Nursing Studies; JAN, Journal of Advanced Nursing; JKAN, Journal of Korean Academy of Nursing; JJNS, Japan Journal of Nursing Science; non-RCTs, non-randomized controlled trial; RCTs, randomized controlled trial.

Table 2.
Top 20 keywords by mean TF–IDF scores in four leading nursing journals between 2015 and 2024
Rank no. JKAN IJNS JAN JJNS
Keywords Mean TF-IDF Keywords Mean TF-IDF Keywords Mean TF-IDF Keywords Mean TF-IDF
1 nurse .251 nurse .242 nurse .553 covid19 .135
2 covid19 .133 aged .157 aged .125 nurse .125
3 self-efficacy .115 self-care .133 systematic review .123 aged .095
4 aged .108 dementia .120 qualitative research .110 self-care .076
5 qol .105 hospital .103 self-care .101 qol .075
6 depression .100 literature review .099 midwife .100 nursing student .058
7 validity .084 nursing home .080 covid19 .075 depression .054
8 statistical factor analysis .078 depression .078 instrument development .069 anxiety .050
9 adolescent .078 covid19 .074 critical care .068 validity .048
10 reproducibility of result .069 intensive care .066 meta-analysis .068 social support .047
11 self-care .068 patient safety .065 QOL .067 health education .045
12 health behavior .066 RCTs .063 depression .067 reliability .043
13 reliability .064 QOL .060 self-efficacy .058 diabetes mellitus .043
14 student .059 long term care .054 mental health .058 nursing education .042
15 psychological stress .058 critical care .053 nursing student .055 attitude .041
16 women .058 palliative care .052 dementia .054 neoplasm .041
17 child .054 pressure ulcer .051 anxiety .049 pain .041
18 psychological adaptation .050 self-efficacy .049 burnout .046 self-efficacy .040
19 knowledge .048 anxiety .048 nursing education .044 Japan .039
20 neoplasm .048 pain .047 hospital .044 child .039

IJNS, International Journal of Nursing Studies; JAN, Journal of Advanced Nursing; JJNS, Japan Journal of Nursing Science; JKAN, Journal of Korean Academy of Nursing; RCTs, randomized controlled trial; QOL, quality of life; TF-IDF, term frequency–inverse document frequency.

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        Ten-year trends in research designs and keywords: a bibliometric comparison of the Journal of Korean Academy of Nursing and leading international nursing journals
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      Ten-year trends in research designs and keywords: a bibliometric comparison of the Journal of Korean Academy of Nursing and leading international nursing journals
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      Fig. 1. Changes in the distribution of study design types in the four major nursing journals, comparing the 2015–2019 and 2020–2024 periods. (A) Journal of Korean Academy of Nursing (JKAN): Study designs comparison (2015–2019 vs. 2020–2024). (B) International Journal of Nursing Studies (IJNS): Study designs comparison (2015–2019 vs. 2020–2024). (C) Journal of Advanced Nursing (JAN): Study designs comparison (2015–2019 vs. 2020–2024). (D) Japan Journal of Nursing Science (JJNS): Study designs comparison (2015–2019 vs. 2020–2024). RCT, randomized controlled trial.
      Fig. 2. Changes in mean term frequency–inverse document frequency (TF-IDF) scores of study keywords across the four major nursing journals, comparing the 2015–2019 and 2020–2024 periods. (A) Journal of Korean Academy of Nursing (JKAN): Study topics comparison (2015–2019 vs. 2020–2024). (B) International Journal of Nursing Studies (IJNS): Study topics comparison (2015–2019 vs. 2020–2024). (C) Journal of Advanced Nursing (JAN): Study topics comparison (2015–2019 vs. 2020–2024). (D) Japan Journal of Nursing Science (JJNS): Study topics comparison (2015–2019 vs. 2020–2024).

      Fig. 1.

      Fig. 2.

      Ten-year trends in research designs and keywords: a bibliometric comparison of the Journal of Korean Academy of Nursing and leading international nursing journals
      Category JKAN (n=621) IJNS (n=1,457) JAN (n=2,926) JJNS (n=518) Total (N=5,522)
      Quantitative research 530 (85.4) 1,254 (86.1) 1,970 (67.4) 450 (86.9) 4,204 (76.0)
       Experimental study
        RCTs 25 (4.7) 167 (13.3) 149 (7.6) 52 (11.6) 393 (9.3)
        Non-RCTs 121 (22.8) 38 (3.0) 93 (4.7) 67 (14.9) 319 (7.6)
       Observational study
        Cross-sectional survey 172 (32.5) 227 (18.1) 702 (35.6) 211 (46.9) 1,312 (31.2)
        Longitudinal survey 21 (4.1) 85 (6.8) 130 (6.6) 29 (6.4) 265 (6.3)
       Review
        Systematic review 6 (1.0) 243 (19.4) 231 (11.7) 11 (2.4) 491 (11.7)
        Meta-analysis 31 (5.8) 213 (17.0) 156 (7.9) 11 (2.4) 411 (9.8)
        Other review (e.g., scoping review, integrative review, literature review, etc.) 14 (2.6) 180 (14.4) 226 (11.5) 13 (2.9) 433 (10.3)
       Methodological study
        Instrument development and validation 78 (14.7) 23 (1.8) 103 (5.3) 36 (8.1) 240 (5.8)
        Concept analysis 10 (1.9) 7 (0.6) 51 (2.5) 3 (0.7) 71 (1.6)
        Other methodology (e.g., diagnostic accuracy study, predictive model validation, intervention tool/protocol development, etc.) 3 (0.6) 0 (0.0) 9 (0.5) 0 (0.0) 12 (0.3)
        Secondary data analysis 33 (6.2) 64 (5.1) 107 (5.4) 14 (3.1) 218 (5.2)
       Special analyses
        Data mining and AI (e.g., text mining, network analysis, machine learning, judgment analysis, etc.) 12 (2.3) 1 (0.1) 7 (0.4) 2 (0.4) 22 (0.5)
        Other special analyses (e.g., cost-benefit analysis, cost-utility analysis, etc.) 4 (0.8) 6 (0.4) 6 (0.3) 1 (0.2) 17 (0.4)
      Qualitative research 74 (11.9) 132 (9.1) 673 (23.0) 57 (11.0) 936 (17.0)
       Phenomenology 31 (41.9) 15 (11.4) 137 (20.4) 8 (14.0) 191 (20.4)
       Grounded theory 20 (27.0) 23 (17.4) 88 (13.1) 12 (21.1) 143 (15.3)
       Ethnography 1 (1.4) 8 (6.1) 43 (6.4) 0 (0.0) 52 (5.6)
       Narrative research 0 (0.0) 4 (3.0) 13 (1.9) 2 (3.5) 19 (2.0)
       Case study 0 (0.0) 10 (7.6) 30 (4.5) 2 (3.5) 42 (4.5)
       Other qualitative approach (e.g., qualitative descriptive, Q-methodology, content/thematic analysis, etc.) 22 (29.7) 72 (54.5) 362 (53.7) 33 (57.9) 489 (52.2)
      Mixed-methods research 15 (2.4) 53 (3.6) 174 (5.9) 10 (1.9) 252 (4.6)
      Other research (discursive paper) 2 (0.3) 18 (1.2) 109 (3.7) 1 (0.2) 130 (2.4)
      Rank no. JKAN IJNS JAN JJNS
      Keywords Mean TF-IDF Keywords Mean TF-IDF Keywords Mean TF-IDF Keywords Mean TF-IDF
      1 nurse .251 nurse .242 nurse .553 covid19 .135
      2 covid19 .133 aged .157 aged .125 nurse .125
      3 self-efficacy .115 self-care .133 systematic review .123 aged .095
      4 aged .108 dementia .120 qualitative research .110 self-care .076
      5 qol .105 hospital .103 self-care .101 qol .075
      6 depression .100 literature review .099 midwife .100 nursing student .058
      7 validity .084 nursing home .080 covid19 .075 depression .054
      8 statistical factor analysis .078 depression .078 instrument development .069 anxiety .050
      9 adolescent .078 covid19 .074 critical care .068 validity .048
      10 reproducibility of result .069 intensive care .066 meta-analysis .068 social support .047
      11 self-care .068 patient safety .065 QOL .067 health education .045
      12 health behavior .066 RCTs .063 depression .067 reliability .043
      13 reliability .064 QOL .060 self-efficacy .058 diabetes mellitus .043
      14 student .059 long term care .054 mental health .058 nursing education .042
      15 psychological stress .058 critical care .053 nursing student .055 attitude .041
      16 women .058 palliative care .052 dementia .054 neoplasm .041
      17 child .054 pressure ulcer .051 anxiety .049 pain .041
      18 psychological adaptation .050 self-efficacy .049 burnout .046 self-efficacy .040
      19 knowledge .048 anxiety .048 nursing education .044 Japan .039
      20 neoplasm .048 pain .047 hospital .044 child .039
      Table 1. Distribution of research designs and methodologies in four leading nursing journals between 2015 and 2024 (N=5,522)

      Values are presented as number (%).

      AI, artificial intelligence; IJNS, International Journal of Nursing Studies; JAN, Journal of Advanced Nursing; JKAN, Journal of Korean Academy of Nursing; JJNS, Japan Journal of Nursing Science; non-RCTs, non-randomized controlled trial; RCTs, randomized controlled trial.

      Table 2. Top 20 keywords by mean TF–IDF scores in four leading nursing journals between 2015 and 2024

      IJNS, International Journal of Nursing Studies; JAN, Journal of Advanced Nursing; JJNS, Japan Journal of Nursing Science; JKAN, Journal of Korean Academy of Nursing; RCTs, randomized controlled trial; QOL, quality of life; TF-IDF, term frequency–inverse document frequency.

      Table 1.

      Table 2.

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