Bibliographic record
Abstract
This paper explores the potential for the built environment to serve as a determinant of mental health for the aging population. The exploration of situational and environmental context factors for health, such as low SES, low social capital, and social isolation has been stressed in health promotion. Specifically, the presence of accessible green space and facilitated interaction with the green space through activities such as horticulture therapy have been shown to be particularly beneficial. The quality of green space, distance to residential areas, and other factors have also been linked to the impact of the presence of green space on mental health. Much evidence indicates that incorporation of this space in cities can result an improvement in mental health through increasing physical activity and decreasing stress. Horticulture therapy has shown to have a positive effect on variables linked to mental health outcomes in older adults. This suggests a potential for inclusion of gardening-based community programming for cities with aging populations. However, there is a need for additional studies to confirm the effect size and find additional causal mechanisms to understand correlations between improved mental health outcomes and green space. There is also a need to consider the ways in which there can be large-scale coordination of policies on urban planning and healthy city design in North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.004 |
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".