Associations between real-time, self-reported adolescent mental health and urban and architectural design concepts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it