A qualitative study of tobacco interventions for LGBTQ+ youth and young adults: overarching themes and key learnings
Bibliographic record
Abstract
BACKGROUND: Smoking prevalence is very high among lesbian, gay, bisexual, transgendered and queer (LGBTQ+) youth and young adults (YYA) compared to non-LGBTQ+ YYA. A knowledge gap exists on culturally appropriate and effective prevention and cessation efforts for members of this diverse community, as limited interventions have been developed with and for this population, and there are very few studies determining the impact of these interventions. This study identifies the most salient elements of LGBTQ+ cessation and prevention interventions from the perspective of LGBTQ+ YYA. METHODS: Three descriptions of interventions tailored for LGBTQ+ YYA (group cessation counselling, social marketing, and a mobile phone app with social media incorporated), were shared with LGBTQ+ YYA via 24 focus groups with 204 participants in Toronto and Ottawa, Canada. Open-ended questions focused on their feelings, likes and dislikes, and concerns about the culturally modified intervention descriptions. Framework analysis was used to identify overarching themes across all three intervention descriptions. RESULTS: The data revealed eight overarching themes across all three intervention descriptions. Smoking cessation and prevention interventions should have the following key attributes: 1) be LGBTQ+ - specific; 2) be accessible in terms of location, time, availability, and cost; 3) be inclusive, relatable, and highlight diversity; 4) incorporate LGBTQ+ peer support and counselling services; 5) integrate other activities beyond smoking; 6) be positive, motivational, uplifting, and empowering; 7) provide concrete coping mechanisms; and 8) integrate rewards and incentives. CONCLUSIONS: LGBTQ+ YYA focus group participants expressed a desire for an intervention that can incorporate these key elements. The mobile phone app and social media campaign were noted as potential interventions that could include all the essential elements.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".