Exploring Intersectional Factors Associated with Mental Health Service Utilization in a Sample of LGBT2Q+ Canadians
Bibliographic record
Abstract
The present thesis explores LGBT2Q+ (lesbian, gay, bisexual, transgender (trans), Two-Spirit, queer/questioning, plus) and racialized mental health service utilization within Canada using intersectionality-informed quantitative methodology, separated into additive and multiplicative stages. Data from the 2020 LGBT2Q+ Health Survey (N = 1542) were analyzed using modified Poisson regression. Additive analyses explored mental healthcare utilization as framed by the Andersen Behavioural Model of Healthcare Utilization categories: predisposing, enabling, and need. Results show that predisposing and need factors are more statistically associated with mental healthcare utilization, and that there are distinct intracategorical (within-group) differences in subgroups, particularly between racialized and Indigenous respondents. Bivariate associations between mental health conditions and predisposing factors further suggest increased mental health needs and mental health service utilization in sexual orientation and gender minorities. The multiplicative stage built upon results from the additive stage to further explore differences in mental health service utilization. Two-way to four-way interaction models of Andersen predisposing factors show persistent trends in identifying as non-White, trans or gender-diverse, more polysexual orientations, and being born outside of Canada as factors associated with increases in likelihood of mental health service utilization. In bivariate analyses, being racialized was associated with lower mental health service utilization as well as mood and anxiety disorders, yet being racialized was associated with an increase in mental health service use when the previously mentioned intersectional factors were considered. Findings demonstrate how plurality of systems of marginalized identities intersect to create distinct 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".