Family support modifies the effect of changes to same-sex marriage legislation on LGB mental health: evidence from a UK cohort study
Bibliographic record
Abstract
BACKGROUND: Many lesbian, gay and bisexual (LGB) individuals continue to experience unique challenges, such as the lack of family support and access to same-sex marriage. This study examines the effect of the introduction of same-sex marriage in the UK (2013-14) on mental health functioning among sexual minorities, and investigates whether low family support may hamper the positive effects of marriage equality legislation among LGB individuals. METHODS: This analysis included LGB participants (n = 2172) from the UK household longitudinal study waves 3-7, comprising two waves before and two waves after marriage equality legislation passed in England, Wales and Scotland. Individual-level mental health functioning was measured using the mental component score (MCS-12) of the Short Form-12 survey. Fixed-effect panel linear models examined the effect of marriage equality on MCS-12 across varying family support levels. Analyses included adjustment for covariates and survey weights. RESULTS: Legalization of same-sex marriage was independently associated with an increase of 1.17 [95% confidence interval (CI): 0.28-2.05] MCS-12 in men and 1.13 (95% CI: 0.47-2.27) MCS-12 in women. For men, each additional standard deviation of family support modified the effect of legalization on mental health functioning by +0.70 (95% CI: 0.22-1.18) MCS-12 score. No interaction was found in women. CONCLUSIONS: Our findings provide evidence that same-sex marriage will likely improve LGB mental health functioning, and these effects may be generalizable to other European countries. Since male sexual minorities with low family support benefited the least, additional interventions aimed at improving family support and acceptance of this group is required to help reduce mental health disparities.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".