The search for healthy schools: A multilevel latent class analysis of schools and their students
Bibliographic record
Abstract
The objective of this study was to establish and investigate a taxonomy of school health among high school students in Ontario, Canada. Data analyzed were based on 3358 9th-12th graders attending 103 high schools who participated in the 2011 Ontario Student Drug Use and Health Survey. Based on 10 health-related indicators, multilevel latent class analysis was used to extract 4 student-level latent classes and 3 school-level latent classes. Unhealthy schools (19% of schools) had the lowest proportion of healthy students (39%) and the highest proportion of substance-using (31%) and unhealthy (18%) students. Healthy schools (66%) contained the highest proportion of healthy students (56%) and smaller proportions of substance-using (22%) and unhealthy students (8%). Distressed schools (15%) were similar to healthy schools in terms of the proportions of healthy and unhealthy students. Distressed schools, however, were characterized by having the largest proportion of distressed students (35%) and the lowest proportion of substance-using students (4%). Meaningful categories of schools with respect to healthy environments can be identified and these categories could be used for focusing interventions and evaluating school health programs.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".