Systematic reviews in medical education: A practical approach: AMEE Guide 94
Bibliographic record
Abstract
The twentieth century saw a paradigm shift in medical education, with acceptance that 'knowledge' and 'truth' are contextual, in flux and always evolving. The twenty-first century has seen a greater explosion in computer technology leading to a massive increase in information and an ease of availability, both offering great potential to future research. However, for many decades, there have been voices within the health care system raising an alarm at the lack of evidence to support widespread clinical practice; from these voices, the concept of and need for evidence-based health-care has grown. Parallel to this development has been the emergence of evidence-based medical education; if healthcare is evidence-based, then the training of practitioners who provide this healthcare must equally be evidence-based. Evidence-based medical education involves the systematic collection, synthesis and application of all available evidence, when available, and not just the opinion of experts. This represented a seismic shift from a position of expert based consensus guidance to evidence led guidance for evolving clinical knowledge. The aim of this guide is to provide a practical approach to the development and application of a systematic review in medical education; a valid method used in this guide to seek and substantiate the effects of interventions in medical education.
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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 | 0.094 | 0.229 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".