Urban vegetation and well‐being: A cross‐sectional study in Montreal, Canada
Bibliographic record
Abstract
Abstract As urbanisation continues to accelerate, urban green spaces are increasingly recognised as key elements for enhancing people's health and well‐being. However, most research has used vegetation metrics that may not capture the specific associations between different types of vegetation and different mental health outcomes. In this study, we investigate the cross‐sectional associations between residential vegetation exposure and individual well‐being in Montreal, Canada, using different vegetation and well‐being measures: The proportion of grass cover, tree cover, and average NDVI value within buffers of various radii (100–1000 m) were linked to each participant's residence (n = 1072, aged 18 years or older), while well‐being was assessed using subjective happiness, emotional well‐being, and personal well‐being scales. The associations were analysed using generalised additive regression models. Our findings show that more vegetation was linked to enhanced well‐being, although the effect sizes were relatively small. Irrespective of the buffer distance, the positive associations for grass and NDVI were more pronounced than those for trees, though these associations varied across the different well‐being outcome measures. We also observed that increasing tree coverage has a stronger positive effect on the well‐being of individuals who are dissatisfied with the current number of street trees. Synthesis and applications. Everyday exposure to nearby nature is associated with better self‐reported mental health, suggesting urban greening policies should focus on including more vegetation within built spaces, from individual street trees to small and large parks. Our study also highlights the importance of distinguishing between different types of vegetation (e.g. grass vs. trees) when studying the effects of vegetation on well‐being or other health‐related outcomes. Likewise, using different measures of well‐being may provide a more nuanced and comprehensive understanding of how vegetation impacts people's well‐being. Read the free Plain Language Summary for this article on the Journal blog.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".