Residential greenness and reduced depression during COVID-19: Longitudinal evidence from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Urban greenness has several demonstrated mental health benefits, including lower rates of depression and loneliness. Few studies have evaluated the possible benefits of greenness on depression during the COVID-19 worldwide pandemic. We investigated this topic using a prospective cohort of Canadian adults. METHODS: Our study population consisted of 13,130 participants, 50 years of age and older, of the Canadian Longitudinal Study on Aging. The Center for Epidemiological Studies Depression Short Scale (CES-D-10) screening tool was used to determine whether individuals had depression at two-time points (pre-pandemic, and 6 months into the pandemic). Greenness was characterized using the maximum annual mean Normalized Difference Vegetation Index (NDVI) (500m buffer) from the pre-pandemic residential address. Logistic regression was used to estimate the odds of depression during the pandemic in relation to an interquartile range increase in the NDVI. RESULTS: The prevalence of depression increased nearly twofold between the pre-pandemic and pandemic surveys (8.5% to 16.5% for men; 14.4% to 27.1% for women). Irrespective of depression status before the pandemic, those with higher residential greenness had lower odds of depression during the pandemic. Among those 'not depressed' pre-pandemic, the odds ratio (OR) of depression during the pandemic in relation to an interquartile increase in the NDVI (0.06) was 0.91 (95% CI: 0.85-0.97), while a weaker association was found for those depressed pre-pandemic (OR=0.96; 95% CI: 0.83-1.11). The inverse association between greenness and depression during the pandemic was strongest among those of lower socioeconomic status. CONCLUSIONS: Our findings suggest that green spaces in urban areas helped mitigate against depression during the pandemic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".