A Geospatial Analysis of Early Psychosis Intervention Programs in Toronto, Canada: Une analyse géospatiale des programmes d’intervention précoce en cas de psychose à Toronto, au Canada
Bibliographic record
Abstract
ObjectivesEarly psychosis intervention (EPI) programs play a crucial role in detecting and treating psychosis early, yet disparities in access persist. This study aimed to assess the spatial accessibility of EPI programs in Toronto, Canada, and to explore the association between access and indicators of neighbourhood-level marginalization.MethodsWe conducted a geospatial analysis using floating catchment area and two-step floating catchment area methods, examining EPI program locations, census population estimates for the 158 Toronto neighbourhoods, and area-level marginalization data. Spatial regression models were used to estimate the association between marginalization factors and spatial accessibility.ResultsOn average, the closest EPI program is 4 km away from the centre of any given neighbourhood (range 0.8-11 km), with variability across the city. Clustering is observed in some neighbourhoods, indicating better spatial accessibility, whereas other neighbourhoods face lower access. A full spatial regression model showed increasing levels of housing and dwelling marginalization, as well as material resource marginalization, to be associated with better access.ConclusionWe identified neighbourhoods that have poorer spatial accessibility to EPI services. Some neighbourhood-level marginalization indicators previously found to be associated with psychosis risk are also associated with better spatial accessibility. It is notable that EPI services in Toronto may be located where they are most needed the most. The study underscores the importance of geospatial analyses to identify and address geographic distance as a potential source of disparity in access.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".