Evaluating and implementing an opportunity for diversity and inclusion in case-based learning
Bibliographic record
Abstract
Problem-based learning (PBL) and case-based learning (CBL) often mention social identities only if this information is directly relevant to diagnosis, which can inadvertently perpetuate stereotypes in trainee learning. Using a student-developed resource entitled "Portraying Social Identities in Medical Curriculum: A Primer," we analyzed cases for social identities, identified gaps, and proposed changes, including use of a validated name bank to reflect diversity as represented by local census data. Through this innovation, suggestions were provided to represent the social determinants of health in CBL cases. Other medical schools can use our innovation to improve the social diversity of their medical curriculums.
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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 | 0.171 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".