Natural environments and perceived health in Metro Vancouver, Canada
Bibliographic record
Abstract
TPS 701: Spatial determinants of population health, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background: Growing evidence demonstrates the health benefits of natural environments (NE), but the effect of different NE types remains relatively unexplored. This study assesses the association between perceived health and different types of NEs by analysing both access (i.e., living within proximity to a public park) and exposure (i.e., high percentage of NE in residential neighbourhood). Methods: We used data from the 2013-2014 Canadian Community Health Survey (n=5,881) on self-reported general and mental health in the last year (5-point ordinal scale from poor to excellent). NE was estimated using a land cover map of Metro Vancouver with 13 classifications, including deciduous trees, conifers, grasses, shrubs, and water. Access was defined as living within 300m of a public park (≥ 1 hectare) and exposure as the percentage of each land cover type within several buffer zones of residential postal codes. Multinomial logistic regression models were used to analyse associations between self-reported general and mental health and access and exposure to NE respectively. Results: Exposure to water was significantly associated with a lower odds of self-reported poor general health, adjusted for confounders (OR = 0.98, 95% CI = 0.96, 0.99). A similar association was found for exposure to some vegetation types, but no consistent trends were found across buffer zones for strength of association by vegetation type. No significant associations were found for access to public parks. Conclusions: This study confirms previous studies showing health effects of water and that daily life exposure to NE may be more important than access to public parks. Further research is needed to establish causality and to expand the knowledge on different NEs’ effect on human health.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".