Associations between real-time, self-reported adolescent mental health and urban and architectural design concepts
Bibliographic record
Abstract
Urban environments influence mental health and development of younger populations. Around 50% of lifetime mental illnesses begin at or prior to the age of 14 years old, and global urbanization trends are forecasted to continue into the coming decades, making links between urban spaces and youth mental health especially important. Little extant research explores links between urban environments, and especially distinct urban design exposures, and adolescent (ages 9–17) mental health. This study uses on-site, ecological momentary assessment (i.e. real-time) surveys and adjusted linear mixed models to explore associations between adolescent (n = 70) mental health indicators and multiple pedestrian design and architecture concepts. Results indicated several significant associations. Notably, spaces high in complexity (visual richness), imageability (distinctiveness), and enclosure (room-like quality) tended to support positive affect. Additionally, mixed built-natural spaces scoring high in scale (pedestrian amenities) and complexity appeared to increase calmness and mitigate anxiousness, while biophilic architecture (nature in built design) seemed to support perceived restorativeness. Practice implications include recommendations for planners to consider implementing natural forms of enclosure (e.g. tall trees), imageability (e.g. natural landmarks), and/or complexity (e.g. variety of gardens/shrubs) in or around spaces frequented by adolescents (e.g. schoolyards) to promote psychological well-being, and design socially supportive environments.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".