Investigating site-level longitudinal effects of population health interventions: Gay-Straight Alliances and school safety
Bibliographic record
Abstract
There is limited research on evaluating nonrandomized population health interventions. We aimed to introduce a new approach for assessing site-level longitudinal effects of population health interventions (SLEPHI) by innovatively applying multiple group multilevel (MG-ML) modeling to repeated cycles of cross-sectional data collected from different individuals of the same sites at different times, a design commonly employed in public health research. For illustration, we used this SLEPHI method to examine the influence of Gay-Straight Alliances (GSAs) on school-level perceived safety among lesbian, gay, and bisexual (LGB) and heterosexual (HET) adolescents. Individual-level data of perceived school safety came from 1625 LGB students (67.4% female; mean age, 15.7 years) and 37,597 HET students (50.2% female; mean age, 15.4 years) attending Grades 7–12 in 135 schools, which participated in 3 British Columbia Adolescent Health Surveys (BCAHS: 2003, 2008, 2013) in Canada. School-level data of GSA length since established were collected by telephone in 2008 and 2014. Nested MG-ML models suggested that after accounting for secular trend, cohort effects, measurement error, measurement equivalence, and student age, GSA length linearly related to increased school-level perceived safety among LGB students ( b = 1.57, SE = 0.21, p < .001, β = 0.32) and also among HET students (β = 0.34 in 2003 & 2013, β = 0.32 in 2008) although statistical differences between years for HET youth were likely due to the large sample size. By conducting MG-ML analysis on repeated cross-sectional surveys, this SLEPHI method accounted for many confounding factors and followed schools for a longer period than most longitudinal designs can follow individuals. Therefore, we drew a stronger conclusion than previous observational research about GSAs and LGB students’ well-being. The SLEPHI method can be widely applied to other repeated cycles of cross-sectional data in public health research.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".