A partnership model of early intervention in psychosis programme – a Canadian experience
Bibliographic record
Abstract
AIM: To describe how a new partnership model of early intervention in psychosis, early intervention in psychosis (EIP) programme delivery in Canada attracted the interest of the community and acquired government funding. METHODS: The process by which a few individuals used a conceptual framework of integrated, collaborative, flexible and recovery focused principles to engage community partners and attract government funding is described. RESULTS: The establishment of a small EIP programme and its expansion to a regional programme serving an area of 20,000 square kilometers and a population of approximately 500,000 people were achieved. A programme specific logic prototype was developed. A synergy of public, private and academic services emerged with an infrastructure for ongoing cohesiveness and productivity. Annual clinic visits increased from 641 in 2002 to 1904 in 2007 and annual new patients enrollments grew from 46 to 128 within the same period. Staffing grew from an interdisciplinary staff of 1.5 full-time equivalent (FTE) to the current 10.0 FTE. CONCLUSIONS: A carefully orchestrated programme organization that is inclusive rather than exclusive can produce a balance of evidence-based best practices in client focused service, community mental health integration and academic productivity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".