Description, evaluation and scale‐up potential of a model for rapid access to early intervention for psychosis
Bibliographic record
Abstract
AIM: This paper aims to describe the entry protocol of the Prevention and Early Intervention for Psychosis Program (PEPP)-Montreal, an early intervention program for psychosis. The protocol is designed to fulfil a key objective of the early intervention movement-reducing delays to accessing high-quality care. The paper also aims to describe how this rapid entry protocol can be deployed in other services interested in reducing delays in initiating treatment. METHODS: PEPP provides rapid, easy access to quality care by placing a single, well-trained professional, the intake clinician, at the point of entry. Anyone can refer a youth directly and without formalities to the intake clinician who responds promptly and sensitively to all help-seeking, whether by a youth, a family member, a school counsellor or anyone acting on behalf of a youth in need. To promote accessibility, PEPP guarantees an initial assessment within 72 hours; maintains relationships with referral sources; and conducts awareness-enhancing outreach activities. RESULTS: Since 2003, PEPP has received 1750 referrals, which have all been responded to within 72 hours. Families have been involved in the intake process in 60% of the cases and hospitalization may have been averted in over half of the referrals originating from emergency-room services. Another indicator of success is the very low turnover in the intake clinician's position. Overall, the PEPP model has succeeded in providing rapid, engaging, easy and youth-friendly access to high-quality care. CONCLUSION: The success of this protocol at PEPP has inspired the entry protocols at other first-episode psychosis services. Its ability to provide rapid, engaging access to high-quality services may allow this protocol to become a model for other early intervention services for psychosis and other mental illnesses.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".