Green space associations with mental health and cognitive function
Bibliographic record
Abstract
Urban green space may be important to mental health, but the association between long-term green space exposures and depression, anxiety, and cognitive function in adults remains unknown. METHODS: We examined 8,144 adults enrolled in the CARTaGENE cohort in Quebec Canada. Average green space and change in green space with residential mobility were assessed using satellite-derived normalized difference vegetation index from 5-year residential address histories. Outcomes included depression and anxiety determined through medical record linkages, self-reported doctor diagnosis of depression, and the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7scales. Cognitive function was available for 6,658 individuals from computerized tests of reaction time, working memory, and executive function. We used linear and logistic multivariate models to assess associations between green space and each mental health and cognitive function measure. RESULTS: In fully adjusted analyses, a 0.1 increase in residential normalized difference vegetation index within 500 m was associated with an odds ratio of 0.85 (95% CI: 0.76, 0.95) for a self-reported doctor diagnosis of depression and 0.81 (95% CI: 0.70, 0.93) for moderate anxiety assessed using the Generalized Anxiety Disorder 7 scale. Other models showed protective effects of urban green space on depression and anxiety but were not statistically significant, and the magnitude of association varied by green space exposure and mental health outcome assessment method. We did not observe any evidence of associations between green space and cognitive function. CONCLUSIONS: We observed some evidence to support the hypothesis that urban green space is associated with decreased depression and anxiety but not cognitive function.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".