Adaptation and Evaluation of Online Self-learning Modules to Teach Critical Appraisal and Evidence-Based Practice in Nursing
Bibliographic record
Abstract
Healthcare professionals need to update their knowledge and acquire skills to continually inform their practice based on scientific evidence. This study was designed to evaluate online self-learning modules on critical appraisal skills to promote the use of research in clinical practice among nurses from Quebec (Canada) and the Basque Country (Spain). The teaching material was developed in Quebec and adapted to the Basque Country as part of an international collaboration project. A prospective pre-post study was conducted with 36 nurses from Quebec and 47 from the Basque Country. Assessment comprised the administration of questionnaires before and after the course in order to explore the main intervention outcomes: knowledge acquisition and self-learning readiness. Satisfaction was also measured at the end of the course. Two of the three research hypotheses were confirmed: (1) participants significantly improved their overall knowledge score after the educational intervention; and (2) they were, in general, satisfied with the course, giving it a rating of seven out of 10. Participants also reported a greater readiness for self-directed learning after the course, but this result was not significant in Quebec. The study provides unique knowledge on the cultural adaptation of online self-learning modules for teaching nurses about critical appraisal skills and evidence-based practice.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".