A nationwide cross-sectional study of 15,611 lesbian, gay and bisexual people in China: disclosure of sexual orientation and experiences of negative treatment in health care
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay and bisexual (LGB) people often face individual- and system-level barriers in health care. However, LGB people's experiences of health care in non-European and non-American settings have been scarcely studied. In China, while it has been estimated that there are at least 70 million gender and sexual minorities, there has been no larger-scale study on LGB people's experiences of health care beyond a focus on gay men and HIV. This study is the first larger-scale quantitative study to investigate LGB people's experiences of health care in China, where non-heterosexuality is officially silenced and the needs of non-heterosexual people are largely ignored by service providers. METHODS: An online survey was designed in joint partnership by academic, community groups and the United Nations Development Programme. Targeted and snowball sampling was adopted for participant recruitment. Such unique cross-sectoral partnership made this research possible in the authoritarian state of China where data collection on LGB people is extremely rare. For the analysis in this paper, a sample of 15,611 Chinese LGB people were included. Frequency and descriptive statistics were conducted to describe the LGB respondents' demographic characteristics and their experiences in health care settings. Chi-square tests were conducted to test how experiences vary across LGB people with different demographic characteristics. RESULTS: More than three quarters of the respondents said they would be willing to disclose to their medical care providers their sexual orientation if asked. However, only 5.7% of the respondents said that medical care providers ever asked them about their sexual orientation. About 8.0% of the LGB people surveyed reported having experienced negative treatment in medical care settings. Six percent (5.7%) of the Chinese LGB people said in accessing mental health care services, they were recommended, coaxed into, or provided conversion therapy for sexual orientation, gender identity or gender expression. CONCLUSIONS: There is a strong need to enhance LGB cultural competence among health care providers. Policymakers in China should also formulate laws, policies, regulations, clearly articulated codes of conduct, and transparent procedures and practices to ensure non-discrimination of LGB people in the health care system, with a particular focus on banning conversion therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".