Investigating the independent and synergistic associations between neighbourhood greenness and physical activity in relation to perceived mental health among adults in Canada
Bibliographic record
Abstract
The relationships among neighbourhood greenness, physical activity, and mental health are unclear; therefore, we examined the independent and synergistic associations between neighbourhood greenness and self-rated mental health among a nationally representative sample of urban-dwelling adults in Canada (18–79 years) from the 2007–2019 Canadian Health Measures Survey (n = 12,531). We assessed neighbourhood greenness using the Normalized Difference Vegetation Index within a 500-meter radius of participants’ residential postal codes. We measured physical activity using accelerometers and determined adherence to the recommended 150-minutes of moderate-to-vigorous intensity physical activity (MVPA) per week. We used weighted logistic regression models to test whether MVPA guideline adherence was an effect modifier in the association between neighbourhood greenness and self-rated mental health, adjusting for individual and neighbourhood characteristics. Neighbourhood greenness (aOR = 0.89 [0.62, 1.29]) and MVPA adherence (aOR = 1.22 [0.89, 1.69]) were not associated with self-rated mental health, and no interaction were found on the additive (Relative Excess Risk Due to Interaction = -0.45 [−1.24, 0.35], Attributable Proportion = -0.38 [−1.02, 0.26], Synergy Index = 0.28 [0.02, 3.20]) or multiplicative (OR = 0.7 [0.4, 1.3]) scales. Engaging in the recommended amount of MVPA did not change the finding that Canadian adults had similar self-rated mental health regardless of their neighbourhood greenness.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".