A first step in system improvement: a survey of Early Psychosis Intervention Programmes in Ontario
Bibliographic record
Abstract
AIM: Ontario, Canada is a large province with a geographically dispersed population. Early psychosis intervention (EPI) programmes are available province-wide, with delivery approaches adapted to context. This study examined EPI programme delivery in relation to recently released provincial EPI Program Standards, and variations based on geographic context. METHODS: The data source was a province-wide key informant survey of early psychosis programmes conducted after release of the Standards. Chi-squared tests compared large- and small-area programmes on selected programme structural features and perceived adherence to 19 service components. RESULTS: Responses were obtained from 52 programme sites, including 21 small-area programmes with 1 to 2 staff. In general, frequency of EPI delivery was highest for individual assessment and treatment components, and moderate for social supports and family support. Implementation was lowest for public education, early detection and recovery planning. Small-area programmes reported lower implementation for over half of the components, with differences statistically significant for psychiatric assessment and physical health monitoring. CONCLUSION: Since the release of the Standards, the Ontario Ministry of Health has partnered with a provincial network of EPI stakeholders to support practice improvement. This survey identified components where more implementation support is needed, overall and for rural area delivery. Ultimately, systematic monitoring of programme fidelity and measuring client outcomes are key to advancing the quality of EPI programme delivery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".