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 [Response To Letter]
Bibliographic record
Abstract
Thank you for your thoughtful response 1 to our study, "Key Learnings and Perspectives of a Newly Implemented Sexand Gender-Based Medicine Modular Course Integrated into the First-Year Medical School Curriculum". 2 We are delighted to hear that our efforts to integrate Sex and Gender-Based Medicine (SGBM) into medical education were appreciated and recognized for their contribution to advancing personalized and equitable healthcare.We greatly value the additional insights you have shared, particularly regarding the need for a more gender-inclusive framework that encompasses the experiences of all sexes and genders, including non-binary and transgender individuals.This is a critical area for future course development, and your suggestions emphasize the importance of expanding our curriculum to ensure inclusivity for all.Regarding the concerns about the perceived marginalization of male participants and the strong focus on female health, we acknowledge the need for a balanced representation of gender perspectives.Your feedback, along with that of our students, will be instrumental as we work to improve inclusivity and ensure that the curriculum addresses a comprehensive understanding of gender-sensitive healthcare practices.We also take seriously your recommendation to incorporate more peer-reviewed research and empirical evidence.Strengthening the scientific foundation of the course is a top priority, and we will explore ways to integrate more robust data on gender disparities and the healthcare experiences of diverse populations.Your point about the demanding nature of the seven 90-minute sessions for first-year students is also well-taken.We will review the course structure to find a balance that allows for an in-depth exploration of key topics without overwhelming students.It is worth noting, however, that our course includes highly interactive teaching modules.These sessions involve students working in small groups of 10-11, with two tutors per group, providing an engaging, hands-on learning experience to reinforce the content covered in the lectures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".