‘I created my own access:’ understanding mental healthcare access experiences among LGTBQ + youth who use cannabis in Canada
Bibliographic record
Abstract
In North America, LGBTQ+ youth have high rates of cannabis use and face mental health issues. We conducted a photovoice study to describe the perspectives, needs, and motivations of forty-six LGBTQ+ youth who use cannabis as they access mental healthcare services. Participants' photographs were discussed in individual semi-structured interviews conducted by peer researchers. Following a thematic analysis of the interview transcripts, we first found that, beyond medication, LGBTQ+ youth sought mental health services facilitating introspection to better understand their sexual and gender identities and mental health. Second, participants sought affirming health professionals but often felt judged by providers. Third, access to desired services was often described as uncertain and taxing, which impacted their mental health. Fourth, participants' agency was determined by their experience with mental health services, which translated into resilience to tackle access challenges and cannabis use to mitigate their mental health struggles. Our findings point to the need for mental healthcare delivery that goes beyond medication provision but which in addition foster therapeutic processes based on a holistic understanding of mental health. A trusting dynamic between health professionals and LGBTQ+ youth is imperative to counteract the feelings of stigma experienced by LGBTQ+ youth using cannabis in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".