The Need for Evidence in Medical Education: The Development of Best Evidence Medical Education as an Opportunity to Inform, Guide, and Sustain Medical Education Research
Bibliographic record
Abstract
The development of the Best Evidence Medical Education (BEME) Collaboration is introduced in the context of other systematic review initiatives, specifically the Cochrane and Campbell collaborations. The commentary addresses two goals: to describe the current status of BEME and to situate BEME in the broader context of the medical education community's need to be accountable, to conduct research to understand educational processes and results, and the key role that medical educational research must play within the quality-improvement agenda. Lessons drawn from the evidence-based practice movement of the last ten years and the current experience with BEME suggest that, although BEME will inform some educational policies and practices, its initial success may be limited because of the paucity of studies that meet current standards for evidence and the great difficulty in conducting methodologically rigorous studies in the complex social interaction called education. Nonetheless, the need exists for medical education research to continue to address key issues in medical education using experimental designs, while at the same time anticipating the need for more situation-specific data to permit educators to monitor and benchmark their existing programs within a quality-improvement and accountability framework. The authors conclude that the very nature of being professional in today's social and fiscal context demands that medical educators provide evidence of effectiveness and efficiency of their programs while at the same time BEME and medical education research continue to grow and mature.
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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 | 0.432 | 0.725 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.026 | 0.040 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.042 | 0.032 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".