Investigating the Interrelationships Among Mental Health, Substance Use Disorders, and Suicidal Ideation Among Lesbian, Gay, and Bisexual Adults in the United States: Population-Based Statewide Survey Study
Bibliographic record
Abstract
Background Mental health disparities have been documented among lesbian, gay, and bisexual (LGB) adults in the United States. Substance use disorders and suicidal ideation have been identified as important health concerns for this population. However, the interrelationships among these factors are not well understood. Objective This study aims to investigate the interrelationships among mental health, substance use disorders, and suicidal ideation among LGB adults in the United States using a population-based statewide survey. Methods Our study was an observational cross-sectional analysis, and the data for this study were collected from a sample of LGB adults who participated in the statewide survey. The survey collected information on mental health, substance use disorders, and suicidal ideation using validated measures. Descriptive statistics and inferential data analysis were conducted to explore the interrelationships among these factors. Results The results showed that LGB adults who reported higher levels of depression and drug abuse and dependence also reported higher levels of suicidal tendency and mental illness. Inferential data analysis using χ2 tests revealed significant differences in depression score (χ22=458.241; P<.001), drug abuse and dependence score (χ22=226.946; P<.001), suicidal tendency score (χ22=67.795; P<.001), and mental illness score (χ22=363.722; P<.001) among the 3 sexual identity groups. Inferential data analysis showed significant associations between sexual identity and mental health outcomes, with bisexual individuals reporting the highest levels of depression, drug abuse and dependence, suicidal tendency, and mental illness. Conclusions This study provides important insights into the interrelationships among mental health, substance use disorders, and suicidal ideation among LGB adults in the United States. The findings underscore the need for targeted interventions and research aimed at addressing the mental health needs of sexual minority populations. Future research should aim to better understand the underlying mechanisms driving these disparities and develop culturally sensitive and tailored interventions that meet the unique needs of LGB individuals. Reducing stigma and discrimination against sexual minority populations is also crucial to improving their mental health outcomes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".