A Media-Based School Intervention to Reduce Sexual Orientation Prejudice and Its Relationship to Discrimination, Bullying, and the Mental Health of Lesbian, Gay, and Bisexual Adolescents in Western Canada: A Population-Based Evaluation
Bibliographic record
Abstract
School interventions to address sexual orientation discrimination can be important tools for fostering inclusive school climate, and improving student wellbeing. In this study, we empirically evaluated a film-based intervention, Out in Schools, designed to reduce sexual orientation prejudice and foster inclusive school attitudes. Our evaluation mapped data about Out in Schools presentations onto student data from the random cluster-stratified, province-wide 2013 British Columbia Adolescent Health Survey (BCAHS) as well as potential confounding variables of Gay-Straight Alliance clubs (GSAs) and inclusive school policies. Outcome measures included past year sexual orientation discrimination, bullying, suicidal ideation, and school connectedness among lesbian, gay, and bisexual (LGB) and heterosexual (HET) students in grades 8 through 12 (ages 13 to 18; unweighted N = 21,075, weighted/scaled N = 184,821). Analyses used complex samples logistic regression, adjusted for sample design, conducted separately by gender and orientation. We found Out in Schools presentations were associated with reduced odds of LGB students experiencing discrimination, and both LGB and HET girl students being bullied or considering suicide, and increased levels of school connectedness, even after controlling for GSAs and policies. Out in Schools appears to have an additive contribution to reducing orientation prejudice and improving LGB and heterosexual student wellbeing within schools.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".