Acceptability of integrating mental health and substance use care within sexual health services among young sexual and gender minority men in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Despite well-established evidence showing that young sexual and gender minority (SGM) men experience disproportionate mental health and substance use inequities, few sexual health services provide mental health and substance use care. This qualitative study examined the experiences and perspectives about integrated care models within sexual health services among young SGM men experiencing mental health and substance use challenges. METHODS: Semi-structured interviews were conducted with 50 SGM men aged 18-30 years who reported using substances with sex in Vancouver, Canada. Interviews were analyzed using thematic analysis. RESULTS: Three themes were identified: 1) participants asserted that their sexual health, mental health and substance use-related health needs were interrelated and that not addressing all three concurrently could result in even more negative health outcomes. These concurrent health needs were described as stemming from the oppressive social conditions in which SGM men live. 2) Although sexual health clinics were considered a safe place to discuss sexual health needs, participants reported not being invited by health providers to engage in discussions about their mental health and substance use health-related needs. Participants also perceived how stigmas associated with mental health and substance use limited their ability to express and receive support. 3) Participants identified key characteristics they preferred and wanted within integrated care, including training for health providers on mental health and SGM men's health and connections (e.g., referral processes) between services. Participants also recommended integrating social support programs to help them address SGM-related social challenges. CONCLUSION: Our findings highlight that SGM men's sexual health, mental health and substance use-related health needs and preferences are interrelated and should be addressed together. Tailored training and resources as well as structural adaptations to improve communication channels and collaborative connections between health providers are required to facilitate the development of integrated care for young SGM men.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".