History’s Toolbox in Health Professions Education: One Skill-Based Session on Social Determinants of Health
Bibliographic record
Abstract
Aimed at clinical educators, this article reports on the use of a single skill-based session that introduces learners in Health Professions Education (HPE) to basic techniques from the discipline of history. The premise of the teaching method is a correspondence between medicine's social determinants of health (SDH) and categories of analysis commonly used by historians. At the center are eight categories, or "tools": social, cultural, intellectual, technological, political, economic, racial/ethnic, and gendered. Like the direct and specific implications of many diagnostic signs, each of these adjectives indicate to historians specific types of factors, or determinants. The intervention employs the demonstration-performance teaching method (explanation, demonstration, supervised practice, and evaluation). After the session, learners are able to: use "history's toolbox" as a systematic method for evaluating socio-cultural phenomena inherent in SDH; differentiate eight types of determinants in a historical case study that represents socio-cultural complexity; recognize how categorization simultaneously enhances some determinants while obscuring others, and how the use of constructed social categories in medicine can function to help and harm patients and populations. The intervention described is rooted in scholarship and theoretical questions belonging to the discipline of history, but these are not discussed. Neither the historical content nor the teaching method described here is appropriate for research or teaching in the discipline of history.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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".