Bibliographic record
Abstract
BACKGROUND: "Literature and medicine" is increasingly common in medical schools but not within medical education research. This absence may relate to it not being problematizable in the quantitative way in which this psychometrically-oriented community tends to conceptualize research questions. METHOD: Databases were searched using relevant keywords. Articles were evaluated using methodologies appropriate to their fields. The resulting information was structured around a framework of construct-appropriate assessment methods. RESULTS: Literature and medicine is intended to develop skills as potential proxy outcomes for important constructs. Proposed tools to assess these skills are difficult to evaluate using the field's traditional quantitative framework. Methodologies derived from the qualitative tradition offer alternative assessment methods. CONCLUSION: The medical education research community should take on the challenges presented by literature and medicine. Otherwise, we run the risk that the current evaluation system will prevent important constructs from being effectively taught and assessed.
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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 | 0.215 | 0.474 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.030 | 0.027 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".