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Record W7162076660 · doi:10.82308/9023

Evaluating mental health services and primary health care use among sexual minority men in Canada

2025· dissertation· en· W7162076660 on OpenAlexaboutno aff
Ivan Marbaniang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSexual minorityMental healthHealth careReproductive healthCohortHealth equitySexual orientationPrimary care

Abstract

fetched live from OpenAlex

Research on sexual minority men in Canada has consistently highlighted that they are disproportionately affected by several mental and physical health conditions compared with heterosexual men. However, relatively less is known about their use of health services. The overarching aim of this thesis is to better understand the use of mental health and primary health care services among sexual minority men, which may inform more equitable health care practices. This aim is consistent with Objective 5 of Canada’s First 2SLGBTQI+ (Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer, Intersex +) Federal Plan, passed in 2022; it seeks to strengthen data to guide evidence-based policy making for sexual minority men. In the first manuscript, we used data on 20,874 heterosexual men and 509 gay and bisexual men (GBM) from the 2015-2016 cycles of the Canadian Community Health Survey (CCHS). Using linear regression, we evaluated the association between the number of mental health consultations and depressive symptom scores. Despite GBM using mental health services more frequently than heterosexual men, we found associations to be comparable between heterosexual men and GBM. We discuss the potential role of the deficiency of 2SLGBTQI+ curricula in professional training on our findings and highlight the need for more research. In the second manuscript, we used data from the Engage Cohort study on 2,371 GBM living in Montreal, Toronto, and Vancouver. We evaluated the mediating role of perceived discrimination secondary to intersections of race and HIV status on use of mental health services. We fit a three-way decomposition of causal mediation effects using the imputation method for natural effect models. Relative to white HIV-negative GBM, we found that white GBM living with HIV had higher mental health services use odds not mediated by perceived discrimination. Among racialized HIV-negative GBM, we found comparable mental health services use odds relative to white HIV-negative GBM, but the odds of use to be significantly mediated by perceived discrimination. Finally, among racialized GBM living with HIV, we found comparable MHS use relative to white HIV-negative GBM with perceived discrimination not significantly mediating the odds of use. Our findings highlight the potential need to address intersecting forms of discrimination in mental health services for multiply marginalized GBM. In the third manuscript, we used data from the 2015-2016 cycles of the CCHS on 41,733 heterosexual men and 1,157 GBM. We evaluated primary care physician consultation frequencies between heterosexual men and GBM. We compared estimates from a traditional Poisson model with those from a data-driven approach (double-selection lasso). We grounded both models on Andersen’s Behavioural Model of Health Services Use. We found that compared with heterosexual men, GBM had a significantly higher number of primary care physician consultations. We contextualise these findings to the current shortage of primary care physicians, discuss what our findings mean for health resource allocation and question the lack of timelier data on primary health care use for GBM

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.037
GPT teacher head0.416
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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