Sleep problems among sexual minorities: a longitudinal study on the influence of the family of origin and chosen family
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that lesbian, gay, and bisexual (LGB) adults experience more sleep problems than the general population. As LGB individuals experience a significantly greater risk of family rejection and low family support, our study investigates the role of family support as a potential determinant of LGB sleep problems over a prolonged period, and whether friend support (i.e. chosen family) can mitigate the effect of low family support. Given the importance of sleep on mental and physical health, study results may help shed light on persistent health disparities across sexual orientations. METHODS: Our sample included 1703 LGB individuals from the UK Household Longitudinal Study (UKHLS). Mixed-effect logistic regressions were used to estimate the effect of family and friend support on the development of sleep problems after 24 months while controlling for potential confounders. A modified Pittsburgh Sleep Quality Index was used to measure 1) presence of any sleep problems, 2) short sleep duration, and 3) poor sleep quality. RESULTS: Family support at baseline was independently associated with all sleep problems in our study after 24-months: 1 SD increase in family support was associated with a 0.94 times lower risk of sleep problems (95% C.I = 0.90-0.98), a 0.88 times lower risk of short sleep duration (95% C.I = 0.81-0.95), and a 0.92 times lower risk of sleep quality (95% C.I = 0.93-0.98). Support from one's chosen family (proxied by friend support) did not mitigate the effects of low family support on sleep problems. CONCLUSIONS: Our study found a consistent effect of family support across all sleep outcomes along with evidence of a persistent effect after 24 months. Our findings point to the importance of targeting family support in designing interventions aimed at reducing LGB sleep problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".