Nurse mentored, student research in undergraduate nursing education to support evidence‐based practice: A pilot study
Bibliographic record
Abstract
OBJECTIVE: The aim was to investigate if an extracurricular research skills development program builds the knowledge, attitudes, and skills (KAS) to support evidence-based practice (EBP). METHODS: Twenty nursing students and six mentors in four teams completed small, student-led research projects over 1 year. Using a mixed-methods design, the knowledge, attitudes, and practice (KAP) survey was administered at three-time points, followed by qualitative interviews. A linear mixed-effects regression model was used to analyze survey data and thematic analysis for qualitative data. RESULTS: The change from the KAP survey from the first to the third time point showed a statistically significant difference following engagement in the program. Qualitative data indicated benefits and challenges to participation for both students and mentors. Mentorship provided students with improved relationships, collaboration, and leadership skills. Students believed the program enhanced their understanding of research and reported increased confidence in using EBP. CONCLUSION: Offering students innovative first-hand experiences with research develops research KAS to support EBP.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".