Cross-sectional survey of education on LGBT content in medical schools in Japan
Bibliographic record
Abstract
OBJECTIVES: We aimed to clarify current teaching on lesbian, gay, bisexual, transgender (LGBT) content in Japanese medical schools and compare it with data from the USA and Canada reported in 2011 and Australia and New Zealand reported in 2017. DESIGN: Cross-sectional study. SETTING: Eighty-two medical schools in Japan. PARTICIPANTS: The deans and/or relevant faculty members of the medical schools in Japan. PRIMARY OUTCOME MEASURE: Hours dedicated to teaching LGBT content in each medical school. RESULTS: In total, 60 schools (73.2%) returned a questionnaire. One was excluded because of missing values, leaving 59 responses (72.0%) for analysis. In total, LGBT content was included in preclinical training in 31 of 59 schools and in clinical training in 8 of 53 schools. The proportion of schools that taught no LGBT content in Japan was significantly higher than that in the USA and Canada, both in preclinical and clinical training (p<0.01). The median time dedicated to LGBT content was 1 hour (25th-75th percentile 0-2 hours) during preclinical training and 0 hour during clinical training (25th-75th percentile 0-0 hour). Only 13 schools (22%) taught students to ask about same-sex relations when obtaining a sexual history. Biomedical topics were more likely to be taught than social topics. In total, 45 of 57 schools (79%) evaluated their coverage of LGBT content as poor or very poor, and 23 schools (39%) had some students who had come out as LGBT. Schools with faculty members interested in education on LGBT content were more likely to cover it. CONCLUSION: Education on LGBT content in Japanese medical schools is less established than in the USA and Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".