Student and educator perceptions of an evidence-based medicine research curriculum: recommendations for research curriculum development
Bibliographic record
Abstract
Background: Evidence-based medicine (EBM) allows physicians to integrate evidence, clinical experience, and patient values into clinical decision making and thus has been readily incorporated into medical education; however, there is limited research capturing the perceptions of both student and educator in their experiences in engaging with an EBM-based research curriculum. Assessing the perceptions of both these key stakeholders represents an important area of research as it can help to inform EBM curriculum integration and evaluation efforts. Methods: This qualitative study utilizes a constructivist framework to assess the thoughts, beliefs, and feelings of students and tutors interacting with a 2-year EBM-based research curriculum. Students completed semi-structured interviews and tutors completed online surveys to explore their perceptions and experiences. Interview transcripts and survey responses were analysed using conventional descriptive content analysis to create a set of recommendations for EBM curriculum development. Results: 13 students and 20 tutors participated, and four major themes were identified. Students noted EBM education was most effective when opportunities existed to apply research skills, complete practical research experience, engage actively in learning, and integrate clinical and research concepts. Tutors found the curriculum to be effective but noted it was challenging to accommodate for the diversity of student knowledge and interest in research. Conclusion: This study provides a general set of recommendations for the design, implementation, and refinement of EBM-based research curricula to facilitate student learning through focusing on 1) research consumption, 2) emphasizing application, 3) emphasizing interactivity, 4) curriculum integration, and 5) catering towards student heterogeneity.
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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 | 0.070 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| 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".