Study the past if you would define the future: Historical methods in medical education scholarship
Bibliographic record
Abstract
INTRODUCTION: A study was conducted to describe the state of historical scholarship in medical education including its strengths and opportunities as well as its shortcomings, lacunae, inattentions, and failings. The study took a particular focus on historical methods and methodologies and whether they have been applied appropriately and with rigour. The study serves as a descriptive scan of historical scholarship, as a guide to the use of historical methods for authors, editors and reviewers, and as a possible course correction for improved standards in approaching and reporting on the past in medical education scholarship. METHOD: A meta-study review was conducted to explore the current state of historical scholarship in medical education, to understand the state of the art, and to improve methodological, analytical and reporting rigour. Structured searches were conducted, returns were filtered for inclusion, and 85 articles and chapters were critically analysed. RESULTS: Although there were some exemplary articles identified, the majority reflected many deficits in scholarly practice. Seven broad issues in historical scholarship in medical education were identified that all spoke to the absence of key dimensions of sound scholarship namely: an explicit methodology, explicit engagement with theory, attention to replicability, reflective critique, sources of evidence, a balanced argument, and attention to positionality. DISCUSSION: The issue at hand is not simply about aligning historical scholarship with the standards of social science (such as engaging with methodology, theory and reflexivity), although the authors argue that scholars should do so where appropriate. Rather, it is about the implications these findings have for future work in the field of medical education in studying its many intertwined histories. To that end, there is a discursive space for historical scholarship in medical education that needs further exploration and development to bring together the best of scholarship from the traditions of medical education and history.
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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 | 0.130 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".