Mental Health & Resiliency: Designing Participatory Nature Dependent Environments and Communities for a Sustainable Future
Bibliographic record
Abstract
Sustainable design trends have historically wended down a road that supports the idea of densely populated urban planning as a strategy for mitigating sprawl. Creation of dense urban areas aims at the reduction of carbon emissions. However, studies show that densely populated areas often come with a panacea of mental health, resiliency, and quality of life ails for a community.The following research explores the possibility of combining densely populated design approaches with ancient community planning methods that encourage relationship building: close contact with natural environments and social interchange. Community planning that also creates a day to day contact with nature could be a crucial strategy for both sustaining healthy ecosystems and the development of sustainable communities. The potential for integrating dependence upon nature within built urban environments, as well as the possibility of positive place-making by harvesting nature dependent cultural and social assets in communities and neighborhoods, is, therefore, a wealthy area worthy of exploration.To explore these areas, mental health research on the effects of nature on the brain, as well as the three leading determinants of social, environmental and economic well-being, worldwide, and the founding cultures of these determinants were reviewed. Resilient indigenous groups and case studies of the happiest nation, of Norway and two leading environmentally sustainable and resilient countries, Costa Rica, Cuba, and New Mexico are examined. The paper provides recommendations for improving mental health and resilience by integrating strategies for nature and community needs in urban planning and built environments design.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".