The intersection of cannabis use and oral health among 2SLGBTQI+ youth: a qualitative study protocol
Bibliographic record
Abstract
BACKGROUND: Two-Spirit, lesbian, gay, bisexual, transgender, queer or questioning, intersex, or other sexual orientations and gender identities (2SLGBTQI+) youth face multiple social and structural disparities and barriers that contribute to a sense of mistrust in the provision of healthcare services, including oral health. Cannabis use is also high among 2SLGBTQI+ individuals; however, unknowns exist regarding recreational use and its impact on oral health. Our research aims to explore the intersectionality of 2SLGBTQI+ youth, oral health, and cannabis consumption. METHODS: This qualitative study, guided by community-based participatory research and interpretative phenomenological approaches, will recruit consenting 2SLGBTQI+ youth, aged 19 and older, accessing services at Youth Opportunities Unlimited in London, Canada. Approximately 25 to 30 participants will be recruited to complete a one-on-one in-depth interview or focus group to collect information on their perception about the relationships between cannabis use and oral health. DISCUSSION: Recognizing the self-perceived pathways through which cannabis impacts oral health will prompt the development of theories, raise awareness, and support advocacy efforts for and by 2SLGBTQI+ youth, while also providing valuable insights for the community and healthcare providers at large.
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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 | 0.043 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".