Associations Between Community-Level LGBTQ-Supportive Factors and Substance Use Among Sexual Minority Adolescents
Bibliographic record
Abstract
Purpose: Using representative school-based data and community-level primary data, we investigated how environmental factors (e.g., school and community climate) might be protective against substance use behaviors among a vulnerable population of adolescents. Methods: We analyzed a sample of 2678 sexual minority adolescents using a combination of student-level data (British Columbia Adolescent Health Survey) and primary community-level data (assessing lesbian, gay, bisexual, transgender, and queer [LGBTQ]-specific community and school environments). Using multilevel logistic regression models, we examined associations between lifetime substance use (alcohol, illegal drugs, marijuana, nonmedical use of prescription drugs, and smoking) and community-level predictors (community and school LGBTQ supportiveness). Results: Above and beyond student characteristics (e.g., age and years living in Canada), sexual minority adolescents residing in communities with more LGBTQ supports (i.e., more supportive climates) had lower odds of lifetime illegal drug use (for boys and girls), marijuana use (for girls), and smoking (for girls). Specifically, in communities with more frequent LGBTQ events (such as Pride events), the odds of substance use among sexual minority adolescents living in those communities was lower compared with their counterparts living in communities with fewer LGBTQ supports. Conclusions: The availability of LGBTQ community-level organizations, events, and programs may serve as protective factors for substance use among sexual minority adolescents. In particular, LGBTQ-supportive community factors were negatively associated with substance use, which has important implications for our investment in community programs, laws, and organizations that advance the visibility and rights of LGBTQ people.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".