Summit on Medical School Education in Sexual Health: Report of an Expert Consultation
Bibliographic record
Abstract
INTRODUCTION.: Medical education in sexual health in the United States and Canada is lacking. Medical students and practicing physicians report being underprepared to adequately address their patients' sexual health needs. Recent studies have shown little instruction on sexual health in medical schools and little consensus around the type of material medical students should learn. To address and manage sexual health issues, medical students need improved education and training. AIM.: This meeting report aims to present findings from a summit on the current state of medical school education in sexual health and provides recommended strategies to better train physicians to address sexual health. METHODS.: To catalyze improvements in sexual health education in medical schools, the summit brought together key U.S. and Canadian medical school educators, sexual health educators, and other experts. Attendees reviewed and discussed relevant data and potential recommendations in plenary sessions and then developed key recommendations in smaller breakout groups. RESULTS.: Findings presented at the summit demonstrate that the United States and Canada have high rates of poor sexual health outcomes and that sexual health education in medical schools is variable and in some settings diminished. To address these issues, government, professional, and student organizations are working on efforts to promote sexual health. Several universities already have sexual health curricula in place. Evaluation mechanisms will be essential for developing and refining sexual health education. CONCLUSIONS.: To be effective, sexual health curricula need to be integrated longitudinally throughout medical training. Identifying faculty champions and supporting student efforts are strategies to increase sexual health education. Sexual health requires a multidisciplinary approach, and cross-sector interaction between various public and private entities can help facilitate change. Areas important to address include: core content and placement in the curriculum; interprofessional education and training for integrated care; evaluation mechanisms; faculty development and cooperative strategies. Initial recommendations were drafted for each.
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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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".