Online training course on critical appraisal for nurses: adaptation and assessment
Bibliographic record
Abstract
BACKGROUND: Research is an essential activity for improving quality and efficiency in healthcare. The objective of this study was to train nurses from the public Basque Health Service (Osakidetza) in critical appraisal, promoting continuous training and the use of research in clinical practice. METHODS: This was a prospective pre-post test study. The InfoCritique course on critical appraisal was translated and adapted. A sample of 50 nurses and 3 tutors was recruited. Educational strategies and assessment instruments were established for the course. A course website was created that contained contact details of the teaching team and coordinator, as well as a course handbook and videos introducing the course. Assessment comprised the administration of questionnaires before and after the course, in order to explore the main intervention outcomes: knowledge acquired and self-learning readiness. Satisfaction was also measured at the end of the course. RESULTS: Of the 50 health professionals recruited, 3 did not complete the course for personal or work-related reasons. The mean score on the pre-course knowledge questionnaire was 70.5 out of 100, with a standard deviation of 11.96. In general, participants' performance on the knowledge questionnaire improved after the course, as reflected in the notable increase of the mean score, to 86.6, with a standard deviation of 10.00. Further, analyses confirmed statistically significant differences between pre- and post-course results (p < 0.001). With regard to self-learning readiness, after the course, participants reported a greater readiness and ability for self-directed learning. Lastly, in terms of level of satisfaction with the course, the mean score was 7 out of 10. CONCLUSIONS: Participants significantly improved their knowledge score and self-directed learning readiness after the educational intervention, and they were overall satisfied with the course. For the health system and nursing professionals, this type of course has the potential to provide methodological tools for research, promote a research culture, and encourage critical thinking for evidence-based decision making.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".