Teaching evidence-based medicine literature searching skills to medical students during the clinical years: a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Constructing an answerable question and effectively searching the medical literature are key steps in practicing evidence-based medicine (EBM). This study aimed to identify the effectiveness of delivering a single workshop in EBM literature searching skills to medical students entering their first clinical years of study. METHODS: A randomized controlled trial was conducted with third-year undergraduate medical students. Participants were randomized to participate in a formal workshop in EBM literature searching skills, with EBM literature searching skills and perceived competency in EBM measured at one-week post-intervention via the Fresno tool and Clinical Effectiveness and Evidence-Based Practice Questionnaire. RESULTS: A total of 121 participants were enrolled in the study, with 97 followed-up post-intervention. There was no statistical mean difference in EBM literature searching skills between the 2 groups (mean difference = 0.007 (P = 0.99)). Students attending the EBM workshop were significantly more confident in their ability to construct clinical questions and had greater perceived awareness of information resources. CONCLUSIONS: A single EBM workshop did not result in statistically significant changes in literature searching skills. Teaching and reinforcing EBM literature searching skills during both preclinical and clinical years may result in increased student confidence, which may facilitate student use of EBM skills as future clinicians.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".