Effect of same-sex marriage legalisation on the health of ethnic minority lesbian, gay and bisexual people: a quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: The UK legalised same-sex marriage in 2014. We examine whether same-sex marriage legalisation (SSML), an exogenous policy change, affected the health outcomes among ethnic minority lesbian, gay, bisexual and other (LGB+) individuals. METHODS: Using the UK Household Longitudinal Survey, we applied the Callaway and Sant'Anna difference-in-differences to compare physical and mental health across (a) ethnic LGB+ individuals (treatment group), (b) ethnic heterosexual individuals (control group 1) and (c) British white LGB+ individual (control group 2). The study cohort (n=7054) comprised individuals aged 16+ years at baseline in 2011, and were employed in the study period (2011-2019). The outcomes included physical component scores from the short-form 12 health survey (physical component score (PCS-12)), long-standing illnesses and psychological distress (General Health Questionnaire (GHQ)). RESULTS: After SSML, the PCS-12 among the ethnic LGB+ individuals improved significantly compared with both ethnic heterosexuals and British white LGB+ individuals (2.081, 95% CI 0.487 to 3.675). While no clear patterns were found for long-standing illnesses, the GHQ in the treatment group had modest decreases relative to ethnic heterosexuals, and relative to British white LGB+ individuals, by year 2 after SSML. CONCLUSION: SSML in the UK led to improved physical functioning and reduced psychological distress in ethnic minority LGB+ individuals. Our study shows that ethnic LGB+ individuals may derive even greater health benefits than British white LGB+ people, providing evidence that SSML may help address racial health inequalities within LGB+ communities. As countries worldwide consider legalising same-sex marriage, it is imperative for policymakers to consider the health consequences for sexual and ethnic minorities.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".