An exploratory study on the teaching of evidence-based decision making
Bibliographic record
Abstract
Background: There is no clear guideline on how to teach students evidence-based decision making (EBDM), so this study aimed to assess the impact of an educational intervention on students’ EBDM skills. Methods: This was an explorative mixed-method study of 12 undergraduate occupational therapy students and their teacher. The teaching was aimed at increasing self-efficacy and cognitive skills in EBDM. Semi-structured interviews were conducted to gather the students’ perceived learning benefits. Before and after the intervention, a self-efficacy questionnaire, a critical thinking test, and scored generic cognitive skills in an argument were used as measures of learning achievements. Content analysis was applied to analyze the interview data. To analyze the quantitative data, the Wilcoxon signed rank test was applied. Results: Following the five teaching sessions, the participants’ experienced (a) an understanding of the value and challenges in individually tailored EBDM, (b) the ability to sort and select information, (c) being more cautious in reasoning and reaching conclusions, and (d) better interaction with clients. These categories were supported by significant increases in measures of self-efficacy and cognitive skills used in EBDM. Active, guided education and working with real clients were reported as powerful stimuli for learning. Conclusion: Critical thinking exercises used in authentic health professional evidence-based decisions are promising methods for promoting EBDM.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".