Using a research coach to enhance evidence-based practice integration in undergraduate nursing
Bibliographic record
Abstract
Background and objective: Coaching contributes to the understanding and application of knowledge in nursing practice. This study aims to examine the implementation of a research coach to enhance evidence-based practice integration in undergraduate nursing.Methods: Design: This study used a quasi-experimental non-equivalent post-test-only design. Settings and participants: Forty second-year undergraduate nursing students were invited to participate in the study at a public university in 2019. Methods: The evidence-based practice (EBP) questionnaire was used, and the primary outcomes were attitudes, skills, and capabilities of EBP. The undergraduate students worked with a third-year level research coach to engage in evidence-based nursing using clinical case studies. Results: The findings expressed the students’ readiness to capture, select, and organize their critical thinking skills through case studies and online discussion. Students perceived that they needed versatile skills in the interpretation and application of evidence-based nursing.Conclusions: A research coach played an essential role for novice student nurses in improving decision-making skills and transition to practice in this setting. The research coach model enables critical thinking and problem-solving skills through interaction and case studies.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".