Prevalence of Depression and Anxiety Among Bisexual People Compared to Gay, Lesbian, and Heterosexual Individuals:A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Over the past decade, evidence has accumulated to suggest that bisexual people experience higher rates of poor mental health outcomes compared to both heterosexual and gay/lesbian individuals. However, no previous meta-analyses have been conducted to establish the magnitude of these disparities. To address this research gap, we conducted a systematic review and meta-analysis of studies that reported bisexual-specific data on standardized measures of depression or anxiety. Of the 1,074 full-text articles reviewed, 1,023 were ineligible, predominantly because they did not report separate data for bisexual people (n = 562 studies). Ultimately, 52 eligible studies could be pooled in the analysis. Results indicate that across both outcomes, there is a consistent pattern of lowest rates of depression and anxiety among heterosexual people, while bisexual people exhibit higher or equivalent rates in comparison to lesbian/gay people. On the basis of empirical and theoretical literature, we propose three interrelated contributors to these disparities: experiences of sexual orientation-based discrimination, bisexual invisibility/erasure, and lack of bisexual-affirmative support. Implications for interventions to improve the health and well-being of bisexual people are proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".