A Canadian Programme for Early Intervention in Non-Affective Psychotic Disorders
Bibliographic record
Abstract
OBJECTIVES: To provide a brief overview of the development of clinical services and research for early intervention in psychotic disorders in Canada; to describe components of a comprehensive clinical/research programme for nonaffective psychotic disorders; and to present a summary of results of clinical and social outcomes achieved. METHOD: This is a descriptive paper providing some details of how clinical services are being developed in Canada and concentrating on one particular early intervention programme, Prevention and Early intervention Programme for Psychoses (PEPP) London, Ontario, which is using a historical control design to evaluate the impact of an assertive approach to community case detection. Components of a phase-specific treatment programme and early case detection are described followed by results based on clinical and psychosocial data collected according to a defined protocol. RESULTS: One year outcome for patients treated in PEPP shows use of low dose, pre-dominantly novel antipsychotics and high (81.5%) retention and remission (75%) rates. Highly significant improvements were also reported for self-rated quality of life and cognition. Duration of untreated psychosis (DUP) and premorbid adjustment were associated with improvement in positive and negative symptoms, respectively. Systemic changes to improve access to the service resulted in substantial increases in number of cases treated and a> 50% decline in DUP. CONCLUSIONS: Phase-specific treatment approach and case identification strategies to reduce delay in treatment are likely to substantially improve outcome in nonaffective psychotic disorders compared with what has been reported with traditional approaches.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.009 | 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".