LGBT Identity, Untreated Depression, and Unmet Need for Mental Health Services by Sexual Minority Women and Trans-Identified People
Bibliographic record
Abstract
BACKGROUND: Previous studies have found that transgender, lesbian, and bisexual people report poorer mental health relative to heterosexuals. However, available research provides little information about mental health service access among the highest need groups within these communities: bisexual women and transgender people. This study compared past year unmet need for mental health care and untreated depression between four groups: heterosexual cisgender (i.e., not transgender) women, cisgender lesbians, cisgender bisexual women, and transgender people. MATERIALS AND METHODS: This was a cross-sectional Internet survey. We used targeted sampling to recruit 704 sexual and gender minority people and heterosexual cisgendered adult women across Ontario, Canada. To ensure adequate representation of vulnerable groups, we oversampled racialized and low socioeconomic status (SES) women. RESULTS: Trans participants were 2.4 times (95% confidence intervals [CI] = 1.6-3.8, p < 0.01) and bisexual people 1.8 times (95% CI = 1.1-2.9, p = 0.02) as likely to report an unmet need for mental healthcare as cisgender heterosexual women. Trans participants were also 1.6 times (95% CI = 1.0-27, p = 0.04) more likely to report untreated depression. These differences were not seen after adjustment for social context factors such as discrimination and social support. CONCLUSION: We conclude that there are higher rates of unmet need and untreated depression in trans and bisexual participants that are partly explained by differences in social factors, including experiences of discrimination, lower levels of social support, and systemic exclusion from healthcare. Our findings suggest that the mental health system in Ontario is not currently meeting the needs of many sexual and gender minority people.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".