Facilitating evidence-based practice integration into teaching and learning: A strategy for nurse educators
Bibliographic record
Abstract
Background: Evidence-based practice (EBP) is highly recommended for health care professionals to apply in their practice. Provision of healthcare should be current and scientifically proven, hence the necessity for nursing education institutions to pursue nurse training programmes incorporating teaching of EBP skills. The purpose was to explore and describe the experiences of nurse educators with the integration of EBP into teaching and learning at a nursing college to develop a strategy to support nurse educators and to recommend the inclusion of EBP skills in the nurse training curriculum. Methodology: A qualitative, descriptive phenomenological research design was employed, utilizing non-probability purposive and snowball sampling techniques to select nurse educators as participants. Data were collected through unstructured in-depth interviews using a grand tour question, a digital recording device, and field notes to record all interviews. Colaizzi’s phenomenological data analysis method guided data analysis using Atlas.ti 24 software for coding. Results: Four themes emerged: nurse educators’ experiences with integration and understanding of the EBP concept, facilitating EBP integration, significance of integrating EBP in teaching and learning, and suggested strategies to enhance EBP integration. Based on these findings, a strategy was developed with seven action statements to support nurse educators. Furthermore, recommendations to the college and future research were suggested for the successful integration of EBP. Conclusions: The suggested strategy and recommendations made, would facilitate and create a culture of EBP within the nursing profession and education.
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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 | 0.121 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".