Don’t just blame the evidence: considering the role of medical education in the poor uptake of evidence-based medicine in clinical practice
Bibliographic record
Abstract
The ‘better evidence for better healthcare manifesto’ recently published in the BMJ considers the importance of generating higher quality research and of ensuring the dissemination of research into relevant, digestible and accessible formats.1 In addition to these important considerations, in order to ‘fix’ evidence-based medicine (EBM) and facilitate evidence-based practice, the strategies currently advocated to teach EBM and the evidence base of the foundational knowledge taught in medical schools need to be addressed. When EBM was originally conceptualised in the 1990s, it was thought that answering questions arising from patient care with the critical appraisal of the primary literature would foster the ‘conscientious, explicit, and judicious use of current best evidence’ in clinical practice.2 In medical schools, this reactive approach to EBM has resulted in the creation of specific ‘EBM curriculum’, either as stand-alone courses or integrated with clinical care, that have focused on the steps of critical appraisal.3 Once ‘trained’, learners have been expected to apply these EBM competencies to address point-of-care questions. However, it was soon pointed out that expecting all practitioners to become enthusiastic consumers of the primary literature was not …
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.406 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".