Experiential Understandings of Urban Public Space and In-Situ Mental Wellbeing for Young Adults Living With Mental Illness
Bibliographic record
Abstract
It has become increasingly vital to promote positive mental wellbeing in cities worldwide as scholars have begun to determine that urban residency is associated with poor mental health, linking urban public space characteristics and usage to mental wellbeing. Participants (Toronto residents aged 18-30 with diagnosed mental illness) used the EthicaData smartphone application to capture in-situ experiential understandings of their everyday experiences in public spaces over two weeks. The research objectives are to 1) understand how public spaces meet (or do not) the specific needs of young Torontonians living with mental illness; and 2) understand how participants’ perceptions of public space as offering connection to nature, promoting physical activity, and facilitating social interaction impacts their mental wellbeing. As a frequently marginalized subpopulation, capturing everyday experiences of individuals living with mental illness in public spaces offers insight into building truly inclusive public spaces for the entire population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".