Assessing the Impact of School-Based Greenness on Mental Health Among Adolescent Students in Ontario, Canada
Bibliographic record
Abstract
Neighbourhood greenness has been frequently associated with improved mental health in adulthood, yet its impact among youth is less clear. Additionally, though youth spend large portions of time at school, no study has investigated associations between school-based measures of greenness and students' mental health in Canada. We addressed this gap by linking participant responses from the 2016-2017 Ontario Student Drug Use and Health Survey to school-based features of the built environment. Our analyses included 6313 students, ages 11-20. Measures of greenness were the mean and max of the annual mean Normalized Difference Vegetation Index within 500 m and 1000 m from the centroid of the school postal code. Measures of mental health included: serious psychological distress (Kessler 6-item Psychological Distress Scale), self-rated mental health (using a five-point Likert scale), suicide ideation, and suicide attempt. In our study population, the prevalence of serious psychological distress and low self-rated mental health was 16.7% and 20.3%, respectively. Suicide ideation was reported by 13.5% of participants, while 3.7% reported a suicide attempt. Quantity of greenness was similar between schools in the lower and upper quartiles. In logistic regressions, we found no association between objective school-based greenness and mental health, as assessed by multiple measures, both before and after adjustment. Null findings held true after stratification by season, as well. Whether other characteristics of school greenness (such as type, quality, or access and use) are more impactful to students' mental health should be a focus of future analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".