Development and evaluation of an evidence-based medicine module in the undergraduate medical curriculum
Bibliographic record
Abstract
BACKGROUND: Evidence-based medicine (EBM) is a core competence in both undergraduate and postgraduate medical curricula. However, its integration into curricula varies widely. Our study will help medical colleges develop, implement and evaluate their EBM courses. We assessed the effectiveness of workshops in improving critical appraisal skills among medical students. METHODS: A before-and-after study design without a control group was used. A 5-week short EBM module including lectures, workshops, and online search sessions was conducted with 52 fourth-year medical students during their primary healthcare course at the College of Medicine, Princess Nourah bint Abdulrahman University. Statistical analysis was performed using SPSS statistical software (version 20, SPSS Inc., Chicago, US). Parametric tests as well as Student's paired t-test for pre- and post-test comparisons were used. RESULTS: Forty-nine (49) participants completed the pre- and post-training Fresno tests, and 44.9% of the participants had a GPA of 4.0 or higher. The mean Fresno test score increased from 45.63 (SD 21.89) on the pre-test to 64.49 (SD 33.31) on the post-test, with significant improvements in the following items: search strategies, relevance, internal validity, magnitude and significance of results, statistical values of diagnosis studies (sensitivity, specificity, and LR), statistical values of therapy studies (ARR, RRR, and NNT), and best study design for diagnosis and prognosis (P < 0.05). CONCLUSION: This study supports that a short course in EBM that is incorporated into the undergraduate curriculum, especially in the clinical years, might be effective in improving medical students' knowledge and skills in EBM. However, prospective studies are necessary to assess the long-term impact of these interventions and ultimately their effectiveness for clinical decision making.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".