Associations Between Community-Level LGBTQ-Supportive Factors and Substance Use Among Sexual Minority Adolescents
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it