Self-directed learning profiles and their associations with research and evidence-based practice competence among clinical nurses: a multicenter cross-sectional study

Self-directed learning competence (SDLC) is essential for nurses’ continuing professional development. This study aimed to identify latent profiles of SDLC among clinical nurses and examine the associations between profile membership and self-reported nursing research competence (NRC) and evidence-based practice competence (EBPC). A multicenter cross-sectional survey was conducted among 486 registered nurses from five general hospitals in Anhui Province, China. Data were collected using a general information questionnaire, the Self-Directed Learning Competence for Nurses Scale , the Nursing Research Competence Self-Rating Scale , and the Questionnaire to Evaluate the Competency in Evidence-Based Practice of Registered Nurses (EBP-COQ-Prof) . Latent profile analysis, Spearman’s rank correlation analysis, and multinomial logistic regression were performed. Three sample-relative SDLC profiles were identified: low (36.6%), moderate (37.0%), and high (26.3%). SDLC was positively correlated with NRC (ρ = 0.469, p < 0.001) and EBPC (ρ = 0.409, p < 0.001). Each 10-point increase in self-reported NRC score was associated with greater odds of membership in the moderate (OR = 1.203, 95% CI: 1.072–1.350) and high (OR = 1.583, 95% CI: 1.373–1.824) profiles. Similarly, each 10-point increase in self-reported EBPC score was associated with greater odds of membership in the moderate (OR = 1.227, 95% CI: 1.029–1.463) and high (OR = 1.749, 95% CI: 1.421–2.153) profiles. Participation in clinical teaching was associated with membership in both the moderate and high profiles. Research involvement was associated with membership in the moderate rather than the low SDLC profile, whereas the association for the high-versus-low contrast did not reach statistical significance. Within the fitted model, more than two-thirds of participants were assigned to the sample-relative low or moderate SDLC profiles. Higher self-reported NRC and EBPC scores and participation in clinical teaching were associated with membership in both the moderate and high SDLC profiles. Research involvement was associated with the moderate-versus-low contrast, whereas the high-versus-low contrast did not reach statistical significance. These profiles may help characterize variation in nurses’ SDLC and generate hypotheses for targeted professional-development strategies, which should be evaluated prospectively.

Authors

Institutions

Publication Details

Journal
BMC Nursing
Published
2026-11-03
DOI
https://doi.org/10.1186/s12912-026-05579-8
Primary Topic
Health Sciences Research and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Self-directed learning profiles and their associations with research and evidence-based practice competence among clinical nurses: a multicenter cross-sectional study

Yuyan Wu, Yefei Wang, ChengChen Wang
BMC Nursing
Health Sciences Research and Education
article

Self-directed learning profiles and their associations with research and evidence-based practice competence among clinical nurses: a multicenter cross-sectional study

Yuyan Wu, Yefei Wang, ChengChen Wang
article en

Abstract

Self-directed learning competence (SDLC) is essential for nurses’ continuing professional development. This study aimed to identify latent profiles of SDLC among clinical nurses and examine the associations between profile membership and self-reported nursing research competence (NRC) and evidence-based practice competence (EBPC). A multicenter cross-sectional survey was conducted among 486 registered nurses from five general hospitals in Anhui Province, China. Data were collected using a general information questionnaire, the Self-Directed Learning Competence for Nurses Scale , the Nursing Research Competence Self-Rating Scale , and the Questionnaire to Evaluate the Competency in Evidence-Based Practice of Registered Nurses (EBP-COQ-Prof) . Latent profile analysis, Spearman’s rank correlation analysis, and multinomial logistic regression were performed. Three sample-relative SDLC profiles were identified: low (36.6%), moderate (37.0%), and high (26.3%). SDLC was positively correlated with NRC (ρ = 0.469, p < 0.001) and EBPC (ρ = 0.409, p < 0.001). Each 10-point increase in self-reported NRC score was associated with greater odds of membership in the moderate (OR = 1.203, 95% CI: 1.072–1.350) and high (OR = 1.583, 95% CI: 1.373–1.824) profiles. Similarly, each 10-point increase in self-reported EBPC score was associated with greater odds of membership in the moderate (OR = 1.227, 95% CI: 1.029–1.463) and high (OR = 1.749, 95% CI: 1.421–2.153) profiles. Participation in clinical teaching was associated with membership in both the moderate and high profiles. Research involvement was associated with membership in the moderate rather than the low SDLC profile, whereas the association for the high-versus-low contrast did not reach statistical significance. Within the fitted model, more than two-thirds of participants were assigned to the sample-relative low or moderate SDLC profiles. Higher self-reported NRC and EBPC scores and participation in clinical teaching were associated with membership in both the moderate and high SDLC profiles. Research involvement was associated with the moderate-versus-low contrast, whereas the high-versus-low contrast did not reach statistical significance. These profiles may help characterize variation in nurses’ SDLC and generate hypotheses for targeted professional-development strategies, which should be evaluated prospectively.

BMC Nursing
University of Science and Technology of China (CN), Anhui Medical University (CN), Anhui Provincial Hospital (CN), Third People's Hospital of Hefei (CN), Hefei First People's Hospital (CN)
Openalex Percentile: Top 10%
Health Sciences Research and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon