Key Learnings and Perspectives of a Newly Implemented Sex-and Gender-Based Medicine Modular Course Integrated into the First-Year Medical School Curriculum: A Mixed-Method Survey
Bibliographic record
Abstract
Purpose: Sex and Gender-Based Medicine (SGBM) addresses the influence of sex and gender on health and healthcare, emphasizing personalized care. Integrating SGBM into medical education is challenging. This study examines the implementation of an SGBM course in an Israeli university during the first year of the medical school. Methods: The course integrated lectures, group work, online gender studies resources, workshops, teacher training, and essential literature. The curriculum spanned pre-clinical and clinical aspects, featuring seven 90-minute sessions. Surveys assessed course structure, content, and lecturers using a 5-point Likert scale and qualitative feedback. Quantitative analysis involved descriptive statistics, and thematic analysis was used for qualitative data. Results: Of the 84 students surveyed, 35 (41.7%) responded to the first part, and 30 (35.7%) to the second. The SGBM course received high satisfaction with an average score of 3.63, surpassing other first-year courses (average 3.21). Students appreciated the supportive academic atmosphere (mean score 4.20) and diverse teaching methods (mean score 4.03), while the development of feminist thinking was less appreciated (average score 3.49). Lecturers received high ratings (average score 4.33). Qualitative feedback highlighted the value of group work, the significance of the subject matter, and the helpfulness of supplementary videos. Students requested more content on contemporary issues like gender transition and patient perspectives. The feminist medicine aspect was contentious, with students seeking better gender balance and scientific evidence. Conclusion: Introducing SGBM into the first-year curriculum yielded positive results with high student satisfaction for content and lecturers. An expanded course module is planned, to be assessed at the end of the next academic year.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".