Green space and mental health for vulnerable populations: A conceptual review of the evidence
Bibliographic record
Abstract
Introduction: Mental health is an essential component of overall health that is affected by various environmental factors. Research suggests the inclusion of green space and nature settings in built environments is beneficial for mental health, particularly for vulnerable populations such as military Veterans. Inequities exist for certain populations in relation to accessing a high quality and quantity of green space. Methods: This conceptual review offers a broad assessment of peer-reviewed literature examining links between green space and mental health. Results: Many studies have highlighted associations between exposure to green space and the mental health of vulnerable populations, such as Veterans and individuals of relatively low socio-economic status (SES). Evidence points to the importance of contextual features of green space, such as quality and quantity of green space, in relation to mental health benefits. Engagement in nature-based outdoor activities in green space, or other nature settings, appears to offer restorative effects linked to cognitive function and mental health benefits. Discussion: There is an emerging body of evidence on the relationship between mental well-being and accessibility to green space and nature settings, particularly for vulnerable populations. More research should focus on accessibility to green space and nature settings for the Veteran population.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".