Technology-Supported Guidance Models to Stimulate Nursing Students’ Self-Efficacy in Clinical Practice: Scoping Review
Bibliographic record
Abstract
BACKGROUND: In nursing education, bridging the gap between theoretical knowledge and practical skills is crucial for developing competence in clinical practice. Nursing students encounter challenges in acquiring these essential skills, making self-efficacy a critical component in their professional development. Self-efficacy pertains to individual's belief in their ability to perform tasks and overcome challenges, with significant implications for clinical skills acquisition and academic success. Previous research has underscored the strong link between nursing students' self-efficacy and their clinical competence. Technology has emerged as a promising tool to enhance self-efficacy by enabling personalized learning experiences and in-depth discussions. However, there is a need for a comprehensive literature review to assess the existing body of knowledge and identify research gaps. OBJECTIVE: The aim of this study is to systematically map and identify gaps in published studies on the use of technology-supported guidance models to stimulate nursing students' self-efficacy in clinical practice. METHODS: This scoping review followed the framework of Arksey and O'Malley and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR). A systematic, comprehensive literature search was conducted in ERIC, CINAHL, MEDLINE, Embase, PsycINFO, and Web of Science for studies published between January 2011 and April 2023. The reference lists of the included papers were manually searched to identify additional studies. Pairs of authors screened the papers, assessed eligibility, and extracted the data. The data were thematically organized. RESULTS: A total of 8 studies were included and four thematic groups were identified: (1) technological solutions for learning support, (2) learning focus in clinical practice, (3) teaching strategies and theoretical approaches for self-efficacy, and (4) assessment of self-efficacy and complementary outcomes. CONCLUSIONS: Various technological solutions were adopted in the guidance models to stimulate the self-efficacy of nursing students in clinical practice, leading to positive findings. A total of 7 out of 8 studies presented results that were not statistically significant, highlighting the need for further refinement of the applied interventions. Nurse educators play a pivotal role in applying learning strategies and theoretical approaches to enhance nursing students' self-efficacy, but the contributions of nurse preceptors and peers should not be overlooked. Future studies should consider involving users in the intervention process and using validated instruments tailored to the studies' intervention objectives, ensuring relevance and enabling comparisons across studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".