Strategies to Create a More Gender Identity Inclusive Learning Environment in Preclinical and Clinical Medical Education
Bibliographic record
Abstract
ABSTRACT: Recognition of the spectrum of gender identities has been a recent phenomenon in the medical profession. Over the past 20 years, medical literature related to gender identity diversity has increased several-fold, yet it more commonly addresses clinical care rather than aspects related to medical education. Medical educators continue to struggle with appropriate language and inclusive approaches when discussing gender-based aspects of medical education. Reproductive health education, including obstetrics and gynecology clerkships, is particularly vulnerable to missteps and anachronisms regarding gender identity.This article aims to provide preclinical and clinical medical educators with strategies to identify and predict situations where missteps related to gender identity inclusivity may occur in their curriculum or learning environment, and to develop approaches to improve gender identity inclusivity within medical education. The authors explore 3 areas that commonly pose challenges for medical educators: inclusive language and terminology, anatomy education, and reproductive genetics and genetic counseling. They hope the tools and strategies provided here will be useful to reproductive health medical educators across specialties to enable the realization of a more inclusive learning environment in reproductive health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".