Mental health crisis and spatial accessibility to mental health services in the city of Toronto: A geographic study
Bibliographic record
Abstract
Abstract: Mental illness includes a wide range of disorders that affect mood, thinking, behaviour and overall wellbeing. One in five Canadians has mental health care needs, many of which are unmet. Within the City of Toronto, the provision of specialized mental health care is delivered by over 100 public and private community service organisations and over 700 physicians with a psychiatric specialization - each providing community-based general or specialised care to residents in need. Research has shown that travel distance is an enabling factor of health service utilisation, thus equitable spatial access to services remains a key priority. Using spatial quantitative methods, this study examines potential spatial accessibility to both general and specialized mental health services within the City of Toronto, and levels of statistical association between access to care and prevalence of mental health crisis events. The main datasets analyzed including geo-referenced Census data and occurrence data on mental health crisis (represented by apprehensions under the Mental Health Act undertaken by the Toronto Police Service). The enhanced two-step floating catchment area (E2SFCA) method is used to model spatial accessibility to mental health services based four modes of transportation: driving, walking, cycling and public transit. Areas that are underserved by mental health specialists and mental health community services are identified and shown to have different socioeconomic characteristics. The study reveals spatially explicit patterns of access to various mental health services in Toronto, providing detailed data to inform the planning of and policy on mental health care delivery concerning severe mental health crisis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".