How to Integrate Sex and Gender Medicine into Medical and Allied Health Profession Undergraduate, Graduate, and Post-Graduate Education: Insights from a Rapid Systematic Literature Review and a Thematic Meta-Synthesis
Bibliographic record
Abstract
Sex and gender are concepts that are often misunderstood and misused, being utilized in a biased, preconceived, interchangeable way. Sex and gender medicine is generally overlooked, despite the profound impact of sex and gender on health outcomes. The aims of the present rapid systematic literature review were (i) to assess the extent to which sex- and gender-sensitive topics are covered in medical courses; (ii) to assess the need for and willingness toward integrating/incorporating sex and gender medicine into health-related education; (iii) to identify barriers and facilitators of the process of implementation of sex and gender medicine in medical teaching, mentoring, and training; and (iv) to evaluate the effectiveness of interventional projects targeting curriculum building and improvement for future gender-sensitive physicians. Seven themes were identified by means of a thematic analysis, namely, (i) how much sex- and gender-based medicine is covered by medical courses and integrated into current medical curricula, (ii) the knowledge of sex and gender medicine among medical and allied health profession students, (iii) the need for and willingness toward acquiring sex- and gender-sensitive skills, (iv) how to integrate sex- and gender-based medicine into medical curricula in terms of barriers and facilitators, (v) existing platforms and tools to share knowledge related to sex and gender medicine, (vi) sex- and gender-based medicine aspects in the post-medical education, and (vii) the impact of sex- and gender-sensitive topics integrated into medical curricula. Based on the identified gaps in knowledge, further high-quality, randomized trials with larger samples are urgently warranted to fill these gaps in the field of implementation of gender medicine in educating and training future gender-sensitive physicians.
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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 | 0.123 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".