Are sexual and reproductive health and rights taught in medical school? Results from a global survey
Bibliographic record
Abstract
Abstract Our aim was to investigate the inclusion of sexual and reproductive health and rights (SRHR) topics in medical curricula and the perceived need for, feasibility of, and barriers to teaching SRHR. We distributed a survey with questions on SRHR content, and factors regulating SRHR content, to medical universities worldwide using chain referral. Associations between high SRHR content and independent variables were analyzed using unconditional linear regression or χ2 test. Text data were analyzed by thematic analysis. We collected data from 219 respondents, 143 universities and 54 countries. Clinical SRHR topics such as safe pregnancy and childbirth (95.7%) and contraceptive methods (97.2%) were more frequently reported as taught compared with complex SRHR topics such as sexual violence (63.8%), unsafe abortion (65.7%), and the vulnerability of LGBTQIA persons (23.2%). High SRHR content was associated with high‐income level (P = 0.003) and low abortion restriction (P = 0.042) but varied within settings. Most respondents described teaching SRHR as essential to the health of society. Complexity was cited as a barrier, as were cultural taboos, lack of stakeholder recognition, and dependency on fees and ranking.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".