Heteronormativity and cisgenderism in medical training: A scoping review of lesbian, gay, bisexual, transgender, queer and plus (LGBTQ+) issues in medical training in Asia
Bibliographic record
Abstract
Lesbian, gay, bisexual, transgender, queer and plus (LGBTQ+) related health concerns in medical training have historically been underrepresented or largely omitted. This review goes beyond the Global North and is one of the first scoping reviews to take a regional approach to understanding LGBTQ+ issues in medical training in Asia, a region that millions of LGBTQ+ people call home. A scoping review of peer-reviewed articles published from 2000 to 2024 on LGBTQ+ issues in medical training (including medical, nursing and dentistry) in Asia was conducted. A diversity of attitudes towards LGBTQ+ issues were found among medical, dental, and nursing students. Negative attitudes, especially pathologization of LGBTQ+ people, were still evident. Despite receiving inadequate training from their medical curriculum, students generally showed a strong eagerness to learn more about LGBTQ+ healthcare to know how to act professionally. Although LGBTQ+ students perceived a supportive environment among their peers, there were constant worries about how they were perceived as doctors by attending physicians and patients. Medical, dental, and nursing educators in the identified studies had minimal knowledge of LGBTQ+ issues and limited experience working with LGBTQ+ patients. Articles found that LGBTQ+ issues were lacking in the formal medical curriculum, with very little consideration beyond strictly biomedical concerns. Importantly, this paper debunks the idea that Asia is uniformly negative and conservative on LGBTQ+ issues, highlights the importance of regionally and culturally specific factors in understanding the medical training environment, and provides suggestions for practice and further research. Altogether, this paper argues that there is an urgent need and a substantial opportunity to make medical training in Asia more LGBTQ+ inclusive. • Some medical, dental, and nursing students in Asia hold pathologizing views of LGBTQ+ people and patients. • Medical, dental, and nursing educators in Asia have little knowledge about LGBTQ+ issues and some hold prejudices. • Medical, dental, and nursing students in Asia receive inadequate training about LGBTQ+ issues. • Medical, dental, and nursing students in Asia show a strong eagerness to learn about LGBTQ+ healthcare. • There is an urgent need and significant opportunity to make medical training in Asia more LGBTQ+ inclusive.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".