Early Intervention for Psychosis in Canada
Bibliographic record
Abstract
OBJECTIVE: Early intervention services (EIS) for psychosis have been developed in several countries, including Canada. There is some agreement about the program elements considered essential for improving the long-term outcomes for patients in the early phase of psychotic disorders. In the absence of national standards, the current state of EIS for psychosis in Canada needs to be examined in relation to expert recommendations currently available. METHOD: A detailed online benchmark survey was developed and administered to 11 Canadian academic EIS programs covering administrative, clinical, education, and research domains. In addition, an electronic database and Internet search was conducted to find existing guidelines for EIS. Survey results were then compared with the existing expert recommendations. RESULTS: Most of the surveyed programs offer similar services, in line with published expert recommendations (i.e., easy and rapid access, intensive follow-up through case management with emphasis on patient engagement and continuity of care, and a range of integrated evidence-based psychosocial interventions). However, differences are observed among programs in admission and discharge criteria, services for patients at ultra high risk (UHR) for psychosis, patient to clinician ratios, accessibility of services, and existence of specific inpatient units. These seem to diverge from expert recommendations. CONCLUSIONS: Although Canadian programs are following most expert recommendations on clinical components of care, some programs lack administrative and organizational elements considered essential. Continued mentoring and networking of clinicians through organizations such as the Canadian Consortium for Early Intervention in Psychosis (CCEIP), as well as the development of a fidelity scale through further research, could possibly help programs attain and maintain the best standards of early intervention. However, simply making clinical guidelines available to care providers is not sufficient for changing practices; this will need to be accompanied by adequate funding and support from organizations and policy makers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".