The Impact of Urban Green Space Accessibility on High School Students' Mental Health in Toronto
Bibliographic record
Abstract
Within the vibrant urban fabric of Toronto, Ontario, the dearth of green spaces presents a significant contributing factor to numerous mental health challenges faced by students within the Toronto District School Board. This study delves into the intricate relationship between green spaces and mental health outcomes within Toronto's diverse wards. Building upon prior research, our investigation evaluates the Green Space Index (GSI) and Urban Development (UD) metrics across the city's 22 wards, aiming to elucidate the interplay among these variables. Using data from the 2021 Toronto District School Board (TDSB) Winter Check-in survey and established environmental indices, we explore how accessibility and distribution of green spaces correlate with high school students' mental well-being, specifically feelings of happiness. Contrary to prevailing assumptions, our findings do not substantiate a statistically significant relationship between GSI, UD, and mental health metrics. Multiple regression analysis underscores the limited predictive power of these variables, even when considering income levels, while the Pearson correlation matrix reveals weak associations among studied variables. Our research highlights the complexity of factors influencing mental well-being and emphasizes the need for holistic approaches to urban planning that integrate socio-environmental considerations. Yet, further research is warranted to deepen understanding of environmental influences on mental health and inform strategies for fostering resilient, inclusive urban communities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".