INTRODUCTION
1. Background
Nursing research plays a critical role in providing the scientific foundation for nursing practice and contributing to the development of healthcare policies [1]. The value of scientific research depends on the credibility and reproducibility of its findings, which begin with transparent and complete reporting of research methods and results [2]. In particular, observational studies are subject to less control than experimental studies and are therefore more vulnerable to bias, making clear and detailed reporting of study design, conduct, and analysis especially important [3]. However, many studies fail to report essential methodological information adequately or completely [4,5], which may limit the interpretation and application of research findings.
To address these challenges, international reporting guidelines have been developed to improve the quality of research reporting. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network provides a comprehensive collection of reporting guidelines tailored to various study designs [6]. The adoption of reporting guidelines enhances transparency, completeness, and reproducibility of research, improves the quality of systematic reviews and meta-analyses, and ultimately strengthens the foundation of evidence-based practice [7,8]. Indeed, several studies have demonstrated a positive association between adherence to reporting guidelines and the overall quality of published articles [9,10]. Therefore, evaluating the extent to which reporting guidelines are followed is essential.
One of the most frequently employed research designs in nursing is the descriptive survey study, among which cross-sectional studies are particularly useful for examining participants’ characteristics, health status, attitudes, and behaviors at a specific point in time and for exploring relationships among variables [11,12]. Cross-sectional studies can be conducted within a relatively short time frame and at lower cost, and they are effective in identifying risk factors for diseases or health-related problems [13]. In addition, cross-sectional studies play an important role in nursing research by facilitating the exploration of emerging phenomena, generating hypotheses, and providing foundational data for subsequent longitudinal or interventional studies [14]. Despite these advantages, cross-sectional studies have inherent methodological limitations, including difficulty in inferring causal relationships, inability to establish temporal sequences, and increased susceptibility to bias [15]. Therefore, it is particularly important to clearly recognize these limitations and to report the design, conduct, analysis, and interpretation of cross-sectional studies accurately and transparently.
The international reporting guideline for cross-sectional studies is the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. Developed in 2007, the STROBE guidelines have been established as an international standard for improving the reporting quality of observational studies [6]. The STROBE statement provides a structured checklist designed to enhance the quality of reporting in cross-sectional studies and to help researchers systematically address key information that is frequently omitted [16]. However, studies examining adherence to the STROBE guidelines indicate that compliance remains suboptimal across many academic journals. Systematic reviews of international medical and nursing journals have reported that only approximately 50%~70% of STROBE checklist items are adequately reported, with particularly poor reporting observed for items related to clear description of study design, sample size justification, methods for controlling bias, and handling of missing data [17,18]. Therefore, there is a clear need to assess journal-specific adherence to international reporting guidelines for cross-sectional studies.
The quality of an academic journal is largely determined by the methodological rigor and transparency of reporting in the articles it publishes, which in turn directly influence the journal’s impact, citation rates, and international recognition [19]. Despite the availability of established reporting guidelines, systematic evaluations of how faithfully published articles adhere to these guidelines remain limited. In Korea, nursing journals have experienced substantial quantitative growth over the past several decades, accompanied by a steady increase in the number of journals indexed by the National Research Foundation of Korea [20]. Since its establishment in 1999, the Journal of Korean Biological Nursing Science (JKBNS) has served as a leading journal in basic nursing research, contributing to the strengthening of the scientific foundation of nursing through the publication of interdisciplinary studies integrating basic medical sciences—such as physiology, pathology, pharmacology, microbiology, and anatomy—with nursing science [21].
However, improving journal quality requires more than an increase in publication volume; it necessitates a systematic examination of research designs and levels of evidence. In particular, there has been a lack of comprehensive analysis regarding the extent to which articles published in the JKBNS adhere to international reporting guidelines appropriate to their respective study designs. Therefore, this study aims to evaluate the quality of published articles based on established reporting guidelines and to propose practical strategies for enhancing the overall quality of research reporting in the journal.
Meanwhile, the methodological quality of cross-sectional studies is commonly assessed using appraisal tools developed by the Joanna Briggs Institute (JBI) [22] and the National Institutes of Health (NIH) [23]. However, these tools primarily focus on evaluating the rigor of study design and conduct, and thus differ in purpose and scope from reporting guidelines such as STROBE. Accordingly, mapping STROBE checklist items to the JBI and NIH appraisal criteria at the item level can provide important insights into the conceptual overlap and distinctions between reporting guidelines and methodological quality assessment tools, thereby clarifying the respective roles and limitations of each instrument.
Accordingly, this study aimed to systematically assess the reporting quality of cross-sectional studies published in the JKBNS from 2011 to 2024 using the STROBE reporting guideline. Furthermore, item-level mapping between the STROBE checklist and the JBI and NIH appraisal tools was performed. The findings of this study are intended to inform practical strategies to enhance the reporting quality of articles published in the journal.
2. Study aim
This study aimed to evaluate item-level reporting compliance with the STROBE checklist in cross-sectional studies published in the JKBNS between 2011 and 2024, and to identify strengths and gaps in reporting practices. In addition, this study mapped STROBE checklist items at the item level to the JBI and NIH cross-sectional study appraisal tools. This study seeks to provide empirical evidence on the current level of reporting transparency in cross-sectional studies published in JKBNS and to offer practical insights for improving reporting standards.
METHODS
1. Study design
This study was conducted as a descriptive methodological review to evaluate the reporting quality of cross-sectional survey studies published in JKBNS from 2011 to 2024, based on the STROBE checklist.
2. Data source and search strategy
The literature search for this study was conducted from November 10 to November 15, 2025. The data source was the official online archive of JKBNS (https://jkbns.org/articles/archive.php). For this study, all articles published in JKBNS between 2011 and 2024 were retrieved, and a comprehensive dataset was constructed using Microsoft Excel (Office 365; Microsoft Corporation, Redmond, WA, USA). The dataset included bibliographic information such as publication year, authors, article title, DOI, and study design. During the initial screening phase, titles and abstracts were reviewed using the keywords “cross-sectional,” “survey,” and “descriptive study.” Out of a total of 479 published articles, 192 were identified as cross-sectional survey studies and were considered potentially eligible for inclusion.
3. Eligibility criteria
Studies were included in the analysis if they met the following criteria: (1) published in JKBNS between 2011 and 2024; and (2) explicitly identified as cross-sectional survey studies through the use of terms such as “cross-sectional,” “survey,” or “descriptive study” in the title, abstract, or methods section. Studies were excluded if they met any of the following criteria: (1) review articles, including literature reviews, integrative reviews, comprehensive reviews, or brief reviews; (2) experimental studies, such as randomized controlled trials or quasi-experimental designs; (3) studies involving animal subjects; (4) studies based on secondary data analysis; and (5) cohort studies. The study selection process is illustrated in Figure 1.
4. Instruments
1) STROBE checklist
Although observational studies are widely conducted in health care research, concerns have repeatedly been raised that published reports often provide insufficient information to appraise the strengths and limitations of the findings and to judge their generalizability [6]. The STROBE development process was initiated through a consensus-based approach at a workshop held in September 2004, which involved methodologists, researchers, and journal editors; the checklist was subsequently finalized through iterative revisions informed by empirical evidence and methodological considerations [24]. STROBE was developed to cover three major observational study designs—cohort, case-control, and cross-sectional studies—and its final output is a 22-item checklist [6]. The STROBE checklist is organized to align with the conventional structure of research articles across six domains: title/abstract (item 1), introduction (items 2,3), methods (items 4~12), results (items 13~17), discussion (items 18~21), and other information (item 22). Of the 22 items, 18 apply to all study designs, whereas four items (6,12,14, and 15) are design-specific, with reporting requirements that vary by cohort, case-control, and cross-sectional studies [6]. Several items are further subdivided into sub-items (e.g., a-e), and when these sub-items are treated as individual assessment units for the cross-sectional version, the checklist comprises a total of 32 evaluable sub-items [25]. Importantly, STROBE is intended to facilitate sufficiently complete and transparent reporting of conducted studies rather than to prescribe how studies should be designed or carried out [25]. In this study, adherence to each STROBE item was assessed as “yes” when fully reported, “no” when not reported, and “NA” when not applicable; “yes” and “no” were scored as 1 and 0 points, respectively, and NA items were excluded from scoring. The reporting adherence score (or proportion) was calculated for each article using only applicable items as the denominator [26]. In the present review, only the cross-sectional study version of STROBE was applied.
2) JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies
JBI provides standardized critical appraisal checklists tailored to specific study designs to systematically assess the methodological quality of included studies, particularly in the context of evidence synthesis, where the risk of bias is a central concern. Among these, the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies is designed to evaluate the potential for bias in analytical cross-sectional studies that examine etiology, risk factors, or associations between exposures and outcomes. This checklist was developed by JBI in collaboration with methodological experts, reviewed by its Scientific Committee, and refined through extensive peer consultation. The tool consists of eight items assessing key methodological domains, including clarity of inclusion criteria (item 1), adequacy of descriptions of study participants and setting (item 2), validity and reliability of exposure measurement (item 3), use of objective and standardized criteria for condition measurement (item 4), identification and management of confounding factors (items 5,6), validity and reliability of outcome measurement (item 7), and appropriateness of statistical analysis (item 8). These items are structured to evaluate common sources of bias in analytical cross-sectional designs, such as selection bias, information bias, and confounding, as well as the overall rigor of data analysis. Each item is rated using one of four response options: Yes, No, Unclear, or Not applicable (NA) [22].
3) NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies
The National Heart, Lung, and Blood Institute (NHLBI), a component of the NIH, has developed and disseminated study design-specific Study Quality Assessment Tools to support evidence synthesis and guideline development by systematically evaluating internal validity and the risk of bias. These tools are intended to identify potential methodological and implementation-related sources of bias (e.g., selection, performance, attrition, measurement/detection bias, and confounding) and to inform an overall judgement of study quality based on the extent to which such limitations may influence interpretation of the findings [23]. In the present study, methodological quality was appraised using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, which comprises 14 items covering: clarity of the research question/objective (item 1); study population and recruitment (definition of the population, participation rate, recruitment from the same population/time period, and consistency of eligibility criteria; items 2~4); justification of sample size or power considerations (item 5); temporality and adequacy of the observation time frame (items 6,7); assessment of exposure levels (e.g., dose-response; item 8); validity and reliability of exposure measurement (item 9); repeated assessment of exposure (item 10); validity and reliability of outcome measurement (item 11); blinding of outcome assessors (item 12); loss to follow-up when applicable (item 13); and measurement and statistical control of key confounders (item 14) [27]. Each item is rated as Yes, No, or Other, where Other includes cannot determine (CD), not reported (NR), and NA; item-level judgements are then synthesized into an overall rating of Good/Fair/Poor, with Good indicating the lowest risk of bias and highest confidence in validity, Fair indicating some susceptibility to bias that is unlikely to invalidate the results, and Poor indicating substantial limitations with a meaningful risk of bias [7]. Given that exposures and outcomes are frequently assessed at the same time point in cross-sectional studies, the NHLBI guidance recommends rating the temporality question (item 6) and the time frame question (item 7) as “No” for cross-sectional analyses in which temporal precedence cannot be established [23].
5. Data collection
Data for this study were obtained from the official online archive of JKBNS and systematically organized in a data extraction spreadsheet developed in Microsoft Excel (Office 365; Microsoft Corporation). The spreadsheet captured bibliographic information for all articles published between 2011 and 2024, including author(s), publication year, article title, DOI, and study design. Reasons for exclusion were recorded at each stage of the screening process to ensure traceability, with separate documentation for the first-stage screening (title/abstract review) and the second-stage screening (full-text review). To enhance the accuracy of study design classification, two reviewers independently screened all records using predefined eligibility criteria. During the first screening stage, potentially eligible articles were identified based on titles and abstracts, and full texts were subsequently reviewed in the second stage to determine final inclusion. Discrepancies primarily arose when (1) the cross-sectional survey design was not explicitly stated, (2) it was unclear whether the study was based on primary survey data versus secondary data analysis, or (3) the design could be interpreted as involving follow-up and thus resembling a cohort approach. Such disagreements were resolved through discussion and consensus, informed by methodological descriptions within each article (e.g., study design, participant recruitment, data collection procedures, and analytic plans). For studies included in the final sample, study characteristics were extracted using a standardized template, including Study ID, author(s), publication year, title, study design, sample size, participant characteristics (e.g., sex, age, group), setting, number of independent variables, and statistical methods. One reviewer conducted the initial extraction, and a second reviewer cross-checked the extracted information to correct errors and confirm the final dataset. STROBE-based reporting assessments were conducted by two reviewers. Prior to the main evaluation, the reviewers performed a pilot assessment of 10 articles to calibrate interpretation and establish item-level decision rules. All eligible articles were then assessed independently, and any inconsistencies in item ratings were resolved through 2~3 rounds of discussion until consensus was reached. Throughout the selection and assessment procedures, decisions were documented step-by-step to support methodological transparency and reproducibility. In addition, to ensure conceptual coherence between STROBE reporting items and the methodological quality appraisal tools (JBI and NIH), the two reviewers independently conducted a structured item-mapping procedure and reconciled disagreements through discussion. Specifically, each STROBE item (including cross-sectional sub-items) was examined and compared against corresponding items in the JBI Critical Appraisal Checklist for Analytical Cross Sectional Studies and the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to determine conceptual equivalence or correspondence. The final mapping was confirmed through mutual review and consensus.
6. Data analysis
Data analysis was conducted to systematically summarize the general characteristics of the included observational studies and to assess the completeness of reporting in accordance with the STROBE checklist. In addition, the results of the reporting assessment were examined in relation to the conceptual domains of established observational study quality appraisal tools, namely the JBI Cross-sectional checklist and the NIH Cross-sectional tool, in order to explore areas of conceptual alignment and distinction between reporting guidelines and methodological quality assessment frameworks.
Study characteristics were analyzed using descriptive statistics, including frequencies, percentages, means, and standard deviations. For the assessment of STROBE reporting items, checklist items that were NA were excluded from score calculation. Items that were fully reported were coded as 1, whereas items that were partially reported or not reported were coded as 0. For STROBE items comprising two or more sub-items, an item-level score was calculated as the mean of the corresponding sub-item scores. Item-level STROBE adherence scores were then calculated for each study by summing the item-level scores across all applicable items, and overall reporting performance was summarized using the mean and standard deviation across studies. In addition, the proportion of STROBE items adequately reported was calculated as the percentage of fully reported items relative to the total number of applicable items after excluding NA items. Finally, individual STROBE items were conceptually mapped to the corresponding quality assessment domains of the JBI Cross-sectional checklist and the NIH Cross-sectional tool to compare domains in which reporting completeness and methodological quality overlapped or diverged. Both the STROBE item-level assessment and the conceptual mapping process were independently performed by two reviewers, and inter-rater agreement was subsequently calculated to evaluate the consistency of the assessments.
7. Ethical considerations
This study was conducted as a secondary review using data obtained exclusively from previously published articles and did not involve direct interaction with human participants or the collection of new, identifiable individual-level data. Accordingly, Institutional Review Board (IRB) approval was not required, in line with ethical standards governing reviews of publicly available research materials. All information extracted from the included studies was appropriately referenced to ensure transparency, proper attribution of original sources, and adherence to principles of academic integrity. To reduce the risk of selection or extraction bias, the processes of study selection and data extraction were independently carried out by two reviewers, with discrepancies resolved through discussion.
RESULTS
1. Overview of cross-sectional studies published in JKBNS from 2011 to 2024
A total of 192 cross-sectional studies published in the JKBNS between 2011 and 2024 were included in the analysis (Table 1). By publication period, 82 articles (42.7%) appeared between 2011 and 2015, followed by 67 (34.9%) between 2016 and 2020, and 43 (22.4%) between 2021 and 2024. In terms of study design, the majority were descriptive studies (166, 86.5%), whereas 24 (12.5%) adopted correlational designs. Only one article (0.5%) explicitly reported the use of a reporting guideline. IRB approval was documented in 153 articles (79.7%), and 64 (33.3%) reported receiving research funding. Regarding participant characteristics, 146 articles (76.0%) included both women and men, and 154 (80.2%) focused on adults aged 18 years or older. With respect to participant type, the general population was most frequently examined (71, 37.0%), followed by healthcare professionals and trainees (64, 33.3%) and patients (46, 24.0%); 11 studies (5.7%) did not involve human participants. The mean sample size was 188.0 ± 124.8 participants (range: 6~956), with 121 (63.1%) recruiting fewer than 200 participants. Nearly half were conducted in hospital settings (95, 49.5%), followed by school settings (29.7%) and community or institutional settings (15.6%). The mean number of settings was 3.22 ± 8.32 (range: 1~80), and 107 (55.8%) were carried out in a single setting. The mean number of independent variables examined was 2.36 ± 1.33, with 119 (62.0%) investigating fewer than three independent variables. More than half included a single dependent variable (112, 58.3%). In terms of outcome domains, health-related behaviors (49, 25.5%) were most frequently assessed, followed by physiological and clinical outcomes (30, 15.6%), psychological and psychosocial outcomes (27, 14.1%), and quality of life and well-being (14, 7.3%). Regarding statistical approaches, simple and multiple regression analyses were most commonly applied (74, 38.5%), followed by correlation analyses (41, 21.4%), group difference analyses (39, 20.3%), and logistic regression analyses (25, 13.0%).
2. Assessment of reporting quality based on the STROBE checklist
Table 2 summarizes STROBE item-level reporting compliance among 192 cross-sectional studies published in JKBNS between 2011 and 2024. In the title and abstract domain, the abstract (item 1b) was reported in all studies (100.0%), whereas the title (item 1a) was reported in only 6 studies (3.1%). Both background/rationale (item 2) and objectives (item 3) were consistently reported across all studies. Substantial variability was observed in the Methods domain. While study design (item 4; 99.0%), setting (item 5; 88.5%), and data sources/measurement (item 8; 100.0%) were frequently reported, eligibility criteria (item 6; 60.4%), sample size justification (item 10; 50.0%), and particularly bias (item 9; 9.9%) were infrequently reported. Among statistical method sub-items, subgroup or interaction analyses (item 12b) were reported in 89.6% of studies, whereas handling of missing data, sampling strategy, and sensitivity analyses (items 12c~12e) were not reported in any study (0.0%). Confounding control (item 12a) was reported in 30.2% of studies. In the Results domain, outcome data (item 15) were fully reported in all studies (100.0%), and unadjusted effect estimates (item 16a) and category boundaries (item 16b) were reported in 96.4% and 80.2%, respectively. Reporting of participant flow was incomplete, with flow diagrams (item 13c) reported in only one study (0.5%). In the Discussion domain, key results (item 18) and interpretation (item 20) were consistently reported (100.0%), whereas limitations (item 19; 47.9%) and generalizability (item 21; 51.0%) were reported in approximately half of the studies. Funding information (item 22) was reported in 37.5% of studies. The mean study-level STROBE score was 20.56 ± 2.27 (range: 13~25), and inter-rater agreement was almost perfect (Cohen’s κ = 0.99).
Table 3 summarizes reporting compliance with the 22 STROBE checklist items. The mean study-level STROBE reporting compliance rate was 74.0% ± 7.8% (range: 52.3%~90.3%). Overall, core items within the title/abstract, introduction, results, and discussion domains demonstrated high levels of reporting. In particular, background/rationale, objectives, outcome data, key results, and interpretation items were reported in all studies, corresponding to a compliance rate of 100.0%. In contrast, several items within the Methods domain showed limited reporting. Eligibility criteria were reported in 60.4% of studies, and sample size justification in 50.0%, whereas reporting of bias was notably low at 9.9%. With respect to statistical methods, items related to handling of missing data, sampling strategy, and sensitivity analyses were not reported in any study (0.0%). In addition, reporting of participant flow showed a compliance rate of 54.2%, and reporting of funding information was limited to 37.5% of studies.
3. Mapping STROBE checklist items to the JBI and NIH cross-sectional checklists
Table 4 presents the results of the item-level mapping between the STROBE checklist and two methodological quality appraisal tools, the JBI and the NIH cross-sectional checklist. STROBE items in the Methods domain were mapped to multiple items in both appraisal tools, particularly those related to study setting, participant selection, variable measurement, bias, and statistical methods. Within the Introduction domain, item 3 (objectives) was mapped to item 1 of the NIH tool. In the Methods domain, item 5 (setting) was mapped to item 2 of the JBI checklist and to multiple items in the NIH tool, and item 6 (eligibility criteria) was mapped to items 1 and 2 of the JBI checklist and to items 2 and 4 of the NIH tool. With respect to measurement-related items, item 7 (clearly define all outcomes) and item 8 (data sources/measurement) were mapped to corresponding measurement-related items in both the JBI and NIH tools. Item 9 (bias) showed one-to-many mappings with both appraisal tools. In the JBI checklist, this item was mapped to items addressing participant selection, confounding, outcome measurement, and statistical analysis, and in the NIH tool, it was mapped to items addressing participation rate, outcome measurement, blinding, and confounding adjustment. Item 10 (study size) was mapped to item 5 of the NIH tool, and item 11 (quantitative variables) was mapped to item 4 of the JBI checklist and item 8 of the NIH tool. Item 12 (statistical methods) was mapped to multiple analysis-related items in both appraisal tools. Within the Results domain, item 13 (participants flow diagram) was mapped to items related to participation rate and loss to follow-up in the NIH tool, and items 14~16 (descriptive data, outcome data, and main results) were mapped to result- and analysis-related items in both appraisal tools. In contrast, STROBE items in the Discussion domain (items 18~21) did not have direct counterparts in either the JBI or NIH tools. Item 22 (funding) was mapped to the funding-related item in the NIH tool.
Inter-rater agreement for the item-level mapping was high, with Cohen’s kappa values of κ = 0.85 for the JBI mapping and κ = 0.90 for the NIH mapping, indicating substantial to excellent agreement between the two independent reviewers.
DISCUSSION
This study aimed to evaluate the level of compliance with each item of the STROBE checklist for cross-sectional studies published in JKBNS from 2011 to 2024 and to identify the strengths and weaknesses in reporting. Furthermore, by mapping STROBE checklist items to the JBI and NIH cross-sectional study assessment tools at the item level, it sought to provide empirical evidence on the current state of reporting transparency and derive implications for improving reporting standards.
Based on the findings of this study, the following points can be discussed. Only 0.5% of the papers explicitly reported the use of the STROBE reporting guidelines. This suggests that despite the guidelines being recommended as the international standard for reporting cross-sectional studies, they are not sufficiently recognized or institutionalized in the actual nursing research field. Furthermore, the finding that only 3.1% of papers in this study explicitly stated the research design in the title aligns with previous research indicating low compliance rates for design specification in titles [27]. When the research design is not specified in the title, it can hinder the visibility and usability of the study. Therefore, it is necessary to ensure that the design is clearly stated in the title.
The results in the Methods and Results sections were relatively low in this study. This aligns with previous research indicating that the average compliance scores were lowest in the Results section (36%) and Methods section (49%) [28]. A cross-sectional study has recommended improving transparency, particularly in the Methods and Results sections [29]. Furthermore, the average STROBE reporting compliance rate for research units in this study ranged from 52.3% to 90.3%, showing significant variation. This is similar to findings from a study on STROBE compliance in observational studies published in medical journals, where compliance rates varied from 24% to 68% [28].
Meanwhile, the average STROBE reporting compliance rate among the research units in this study was 74.0%. This was relatively high compared to a previous study analyzing 147 observational studies related to COVID-19 treatment published in Medline in 2020, which reported an average STROBE compliance rate per paper of 45.6% [30]. Furthermore, it was higher than the STROBE compliance rate reported in a previous study evaluating 223 randomly selected articles from three EAACI (European Academy of Allergy and Clinical Immunology) journals, where the overall complete STROBE reporting rate for cross-sectional studies was 45.6% [27].
At the item level, the present study found particularly low reporting rates for bias, with only 9.9% of studies addressing this item. Moreover, none of the included studies reported on the handling of missing data, sampling strategies, or sensitivity analyses. Control of confounding factors was reported in only 30.2% of the studies. These findings are consistent with previous studies that have highlighted insufficient reporting of sensitivity analyses, bias control, and missing data management in observational research [28]. Similar results were also reported in an evaluation of observational studies among Master of Public Health (MPH) theses indexed in Wanfang between January 2014 and May 2019, which identified low compliance rates for methodological and results-related items, including handling of missing data (6.7%), sensitivity analyses (3.6%), participant flow diagrams (15.2%), and reporting of absolute risk (0%) [29].
When reporting on the handling of missing data and sensitivity analyses, as required by the STROBE guidelines, is omitted, the assessment of internal validity and transparency of result interpretation is substantially limited. In particular, without clear information on the patterns and management of missing data, the potential for selection bias cannot be adequately ruled out. Likewise, the absence of sensitivity analyses prevents evaluation of the robustness of the analytical results. These limitations pose a risk of overgeneralization when findings from nursing research are applied to clinical practice or policy decision-making. Therefore, future cross-sectional studies in nursing should adhere more systematically to the STROBE reporting guidelines to enhance transparency and methodological rigor.
The results of this study indicate that reporting on subject flow was insufficient, with only 0.5% of studies presenting a flow diagram. This finding aligns with previous research highlighting inadequate reporting of flow diagrams [28,31]. When the participant recruitment and exclusion processes are not clearly presented, it becomes difficult to assess the potential for selection bias, and limitations arise in the representativeness and reproducibility of the final analysis sample. Particularly in nursing research, where participant heterogeneity is high and research findings are frequently utilized for clinical and policy decision-making, the importance of reporting participant flow charts as recommended by the STROBE guidelines is further emphasized.
The papers evaluated in this study were found to lack sufficient coverage of certain items recommended by the STROBE guidelines. This indicates a deficiency in key elements necessary for readers to assess the validity and applicability of the research [28]. As recommended in the 2020 report that the overall reporting quality of observational studies was weak and required improvement efforts from editors, reviewers, and authors [30], this suggests that educational interventions targeting specific items are necessary.
This study empirically demonstrated the conceptual overlap and distinctions between reporting guidelines and methodological quality assessment tools by comparing and mapping STROBE checklist items with the JBI and NIH cross-sectional study quality assessment tools at the item level. Specifically, the STROBE Method domain corresponded to multiple core items in both quality assessment tools, while items in the Discussion domain showed no direct mapping to either quality assessment tool. The finding that Methods domain items in STROBE were mapped to multiple items in the JBI and NIH tools in one-to-one or one-to-many relationships suggests that key elements of study design and conduct are closely associated with reporting completeness. This implies that studies adhering more rigorously to the STROBE guidelines are more likely to achieve higher ratings in methodological quality assessments.
In contrast, items within the Discussion domain of the STROBE checklist were not directly mapped to either the JBI or NIH quality appraisal tools. This finding suggests that existing methodological quality assessment instruments do not systematically evaluate the transparency of reporting related to result interpretation, acknowledgment of study limitations, and considerations of generalizability. This gap has important implications within the context of nursing research. Nursing research extends beyond the identification of causal relationships and is often conducted to inform clinical practice, support person-centered care, and guide policy and educational decision-making [32,33]. Given these characteristics, transparent reporting of result interpretation, explicit recognition of study limitations, and clear discussion of the generalizability of findings are critical elements that determine not only the scientific merit of research but also its clinical and policy relevance.
Accordingly, nursing journals should actively promote adherence not only to methodological quality appraisal tools but also to the Discussion domain of the STROBE guidelines in order to improve the reporting quality of cross-sectional studies. In addition, while the STROBE guidelines provide a framework for assessing reporting transparency, adherence to these guidelines should not be interpreted as being synonymous with the internal validity or methodological rigor of the original studies.
The present study is subject to certain considerations. The analysis was confined to articles published in a single journal, and adherence to the STROBE reporting guideline should be interpreted carefully, as reporting compliance does not necessarily reflect the internal validity or methodological rigor of the original studies. In addition, changes in STROBE compliance over time were described descriptively, without formal statistical testing of temporal trends. Future research may benefit from the application of longitudinal or time-series approaches to more rigorously examine changes in reporting practices over time.
CONCLUSION
This study evaluated the reporting fidelity of cross-sectional studies published in JKBNS from 2011 to 2024 based on the STROBE checklist and mapped it to the JBI and NIH cross-sectional study assessment tools at the item level. The results showed that while core elements such as study purpose, main results, and interpretation were mostly reported faithfully, reporting imbalances were prominent in the methodology domain. Specifically, reporting on participant selection criteria, sample size justification, consideration of bias, missing data handling, sensitivity analysis, and participant flow diagrams was very limited. This may compromise the transparency of the research process and the validity of result interpretation. The finding that many STROBE items are linked to several methodological items in the JBI and NIH assessment tools suggests that faithful adherence to reporting guidelines is closely related to the methodological quality assessment of research.
In summary, to improve the quality of cross-sectional studies published in JKBNS, a more systematic and explicit application of the STROBE reporting guidelines is necessary. To achieve this, submission guidelines should explicitly reflect compliance with the reporting guidelines, and an education and monitoring system based on these guidelines should be implemented for authors and reviewers. The accumulation of these efforts may substantially contribute to improving the transparency of reporting in cross-sectional studies in nursing.
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