Urban Mental Health Promotion for Marginalized Communities: A Qualitative Study on Age-Friendly, Healthy, and Sustainable Cities
Bibliographic record
Abstract
To reflexively assess what mental health concerns should be addressed for prevention from the perspective of community members when built environment discourse is centered around existing Age-Friendly Cities, Healthy Cities, and sustainable cities policies. A qualitative study was conducted based on the World Health Organization’s Age-Friendly Cities, Healthy Cities, and general sustainable cities policy movements with marginalized community residents expressing concern toward resulting development, resulting in mental health themes emerging organically through participant interviews. A three-phase interview study was conducted with community participants (n = 17); policymakers in the Age-Friendly Cities, Healthy Cities, or sustainable cities movements (n = 5); and with the same cohort of community participants toward exploring oppositional views on development. Participant responses with specific relevance to mental wellbeing were collated for data analysis, toward the reflexive development of themes. Multiple factors beyond greenspace or pollution - such as travel time, risk, and cost - can contribute to daily psychosocial stress. Built environment solutions intended for health promotion may be resisted not because of lack of support for healthy planning, but rather community mental health needs during environmental transition. Greater participatory engagement and community empowerment that is sensitive to diversity in the population will promote mental wellbeing.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".