Medical student exposure to women’s health concepts and practices: a content analysis of curriculum at Canadian medical schools
Bibliographic record
Abstract
Abstract Background Women’s health (WH) includes a broad array of concerns and challenges that affect health across the lifespan. Considerable research shows that women continue to experience disparities in access to and quality of care. Apart from surveys of medical trainees and faculty, little research and none in Canada examined medical curriculum for WH. This study assessed how Canadian medical schools integrate WH in their curriculum. Methods We used deductive and summative content analysis to describe instances and the nature of WH topics in program and course descriptions that were publicly-available on web sites of Canadian medical schools. We reported results using summary statistics and text examples. We employed a framework, tested in our prior research, that included mention of women’s health principles and practices relevant to any health concern or condition including factors (e.g. sex, gender, social determinants) that influence health, and access to or quality of care. Results We retrieved 1459 documents from 16 medical schools (median 49.5, range 16 to 301). Few mentioned WH (125, 8.6 %), and the quantity of mentions varied by school (range 0.0–37.5 %). Pre-clerkship course documents more frequently mentioned WH (61/374, 17.3 %, chi square 43.2, p
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".