How Do Urban Environments Affect Young People’s Mental Health? A Novel Conceptual Framework to Bridge Public Health, Planning, and Neurourbanism
Bibliographic record
Abstract
Childhood and adolescence are crucial periods for mental and social development. Currently, mental illness among young people is a global epidemic, and rates of disorders such as depression and anxiety are rising. Urban living, compared with rural living, is linked with a higher risk of serious mental illness, which is important because the world is urbanizing faster than ever before. Urban environments and their landscapes, designs, and features influence mental health and well-being. However, no conceptual frameworks to date have detailed the effect of urban environments on young people's mental health, and few studies have considered the growing role of digital and social media in this relationship, leading to calls for the development of holistic approaches to describe this relationship. This article synthesizes existing knowledge on urban places (both built and natural environments) and mental health in the public health and urban planning literature and examines the emerging field of neurourbanism (a multidisciplinary study of the effect of urban environments on mental health and brain activity) to enhance current practice and research. We developed 2 novel conceptual frameworks (1 research-oriented, 1 practice-oriented), adapted from Bronfenbrenner's socioecological model, that focus on the relationship between urban environments and young people's mental health. We added a digital and social media contextual level to the socioecological model, and we applied a multilayer concept to highlight potential cross-field interactions and collaborations. The proposed frameworks can help to guide future practice and research in this area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".