Occurrence of multiple mental health or substance use outcomes among bisexuals: a respondent-driven sampling study
Bibliographic record
Abstract
BACKGROUND: Bisexual populations have higher prevalence of depression, anxiety, suicidality and substance use than heterosexuals, and often than gay men or lesbians. The co-occurrence of multiple outcomes has rarely been studied. METHODS: Data were collected from 405 bisexuals using respondent-driven sampling. Weighted analyses were conducted for 387 with outcome data. Multiple outcomes were defined as 3 or more of: depression, anxiety, suicide ideation, problematic alcohol use, or polysubstance use. RESULTS: Among bisexuals, 19.0 % had multiple outcomes. We did not find variation in raw frequency of multiple outcomes across sociodemographic variables (e.g. gender, age). After adjustment, gender and sexual orientation identity were associated, with transgender women and those identifying as bisexual only more likely to have multiple outcomes. Social equity factors had a strong impact in both crude and adjusted analysis: controlling for other factors, high mental health/substance use burden was associated with greater discrimination (prevalence risk ratio (PRR) = 5.71; 95 % CI: 2.08, 15.63) and lower education (PRR = 2.41; 95 % CI: 1.06, 5.49), while higher income-to-needs ratio was protective (PRR = 0.44; 0.20, 1.00). CONCLUSIONS: Mental health and substance use outcomes with high prevalence among bisexuals frequently co-occurred. We find some support for the theory that these multiple outcomes represent a syndemic, defined as co-occurring and mutually reinforcing adverse outcomes driven by social inequity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".