Effects of SGM Education for Undergraduate Medical Students in a Canadian Context
Bibliographic record
Abstract
Medical schools have been striving to equip students with the tools and skills needed to serve patients from the LGBTQ + community, also called the Sexual and Gender Minority (SGM) community. This study aims to assess student comfort with providing care, and faculty knowledge and preparedness in delivering SGM-centered education at our home institution. We conducted two mixed-methods surveys, one geared towards medical students across four years of study and one towards medicine faculty. Each survey collected first demographic information about participants, then used a validated tool to assess knowledge of the SGM community. The qualitative component of both surveys then consisted of a needs assessment to determine what students felt should be changed about their curriculum, and what faculty felt should change about their training to deliver this curriculum. We received 26 student responses from all 4 years of study and 35 faculty responses from a variety of medical specialties. Difference in knowledge assessment scores was not statistically significant across both cohorts. Most students felt overall comfortable providing care for sexual minority individuals, and faculty similarly felt comfortable teaching, but data showcases that perceived comfort is higher among the student cohort. We propose that students are acquiring knowledge and comfort with providing for SGM individuals from sources outside their curriculum, and that additional training of faculty is vital to ensure students not doing this independent learning do not fall through the cracks. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-023-01831-x.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".