Reflections on fostering student nurse evidence-based practice competencies via integration of nursing best practice guidelines
Bibliographic record
Abstract
Evidence-based nursing practice has been identified as an important nurse competency and standard of practice by nurse regulators and nurse educators in both the United States and Canada, yet little is known about the curricular strategies which foster development of evidence-based competencies in the undergraduate nursing context. Although there are several evidence-based practice models that are being used by nurses, much of the literature reflects evidence-based practice implementation strategies which are focused on nurses already in practice. It remains unclear how evidence-based practice competencies are being taught to undergraduate nursing students. In the Canadian context, the Registered Nurses’ Association of Ontario, promotes the implementation of Nursing Best Practice Guidelines as a viable strategy for implementing evidence-base nursing practice in both the clinical and academic contexts. Clinical and academic institutions that implement best practice guidelines and meet the outcome criteria of the Registered Nurses Association may be designated as a Best Practice Spotlight Organization. In this paper, two of the authors reflect on the curricular strategies they used to integrate Best Practice Guidelines into selected undergraduate nursing courses and the challenges and opportunities that this engendered as part of their university school of nursing’s journey to achieve designation as a Best Practice Spotlight Organization (Academic).
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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 | 0.111 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.026 | 0.039 |
| 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".