Health and wellbeing benefits of urban forests in winter: a narrative review
Bibliographic record
Abstract
Urban trees and green spaces, hereafter, urban forests, are known to contribute to human health and wellbeing. However, research has predominantly focused on warm seasons. To understand whether these benefits extend to winter months, when vegetation is dormant, we conducted a narrative review of the health outcomes associated with urban forests in winter in cities with cold climates. We synthesized findings from 21 studies originating from Asia, Europe and North America. The most studied health outcomes were mental health, physical activity and physiological relaxation, all showing a positive relationship with urban forest exposure. These finding appear similar to those observed in warmer seasons. However, more studies are needed, on a diversity of health outcomes, to draw more robust conclusions in this emerging research field. Future research on urban forests should therefore consider winter and the effect of seasonality to improve health and wellbeing of urban dwellers in all seasons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".