Outcomes of a Multiprofessional Educational Intervention in Evidence-Based Practice
Bibliographic record
Abstract
Background: Education is a commonly used intervention in the development of evidence-based practice (EBP). The aim of this study was to evaluate the outcome of an educational intervention on healthcare professionals’ perceived skills in finding, reviewing, and using research evidence in clinical practice. A further aim was to identify potential determinants for the outcome.Methods and Findings: A three-day course in EBP was designed for registered nurses, medical social workers, physiotherapists, occupational therapists, and dieticians. The Developing Evidence-Based Practice Questionnaire (DEBP) questionnaire was administered before and six months after the intervention (N = 274). Non-parametric statistics were used. The results showed an overall effect on ability to find research evidence (p = .0005) and ability to review research evidence (p = .0005), whereas there was no overall effect on use of research evidence in clinical practice (p = .18). However, some subgroups showed a significant improvement over time, for example, those whose profession was nursing or midwifery and those who had experience using evidence-based practice prior to the educational intervention.Conclusions: The results showed that a three-day course in EBP improved the participants’ ability to find and review research evidence, but it did not have an overall effect on the use of research evidence in clinical 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 | 0.006 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".