Détection et intervention précoce pour la psychose : pourquoi et comment ? [Early Detection and Intervention for Psychosis: Why and How?]
Bibliographic record
Abstract
Objectives This article aims to synthesize the critical stages in the development of early detection and intervention services (EIS) for psychosis over the past 30 years, and to review key literature on the essential components and effectiveness of these programs. Method We conducted a narrative review of the literature on the international development of EIS leading to its endorsement as a service delivery model for young people with first-episode psychosis (FEP). We also reviewed various international and Canadian guidelines to identify consensus about the essential components of EIS for psychosis and their effectiveness. Challenges to the implementation of these different essential components are presented, along with practical solutions to addressing them. A particular emphasis is placed on implementing EIS in the Quebec context. Results Based on a model developed in the early 1990s, EIS for psychosis have now been disseminated worldwide and are deployed on a large scale in some regions, such as the United Kingdom and Quebec. The model's gradual expansion has been facilitated by efforts to identify its main objectives and the components essential to achieve them, and by several studies demonstrating its effectiveness. Along with an important philosophical shift to optimism and hope, EIS have typically focused on the twin aims of reducing treatment delay (or the duration of untreated psychosis) and enhancing engagement in specialized, phase-specific, developmentally appropriate treatment. A meta-analysis (published in 2018) demonstrated the superiority of EIS for psychosis compared to standard treatment on several outcomes including hospitalizations, relapse of symptoms, treatment discontinuation, and vocational and social functioning. Based on these studies and expert consensus, many jurisdictions around the world have developed guidelines to ensure compliance with essential components that are associated with the effectiveness of EIS, while accounting for their contextual realities. The components that have been prioritized include outreach to enable early identification and referral; rapid access to care and youth-friendly services; a range of biopsychosocial interventions (pharmacotherapy, cognitive behavioral therapy, psychoeducation, family interventions, integrated substance use interventions, employment and educational support); a shared-decision making approach; and the intensive case management approach adapted to FEP, which are all delivered by an interdisciplinary team. There is also increasing acknowledgement of the value of continuous evaluation that informs treatment decision-making and quality improvement. Conclusion EIS for psychosis have developed gradually and research has demonstrated its effectiveness. Disseminating the model in ways that ensure fidelity to its core values and the implementation of its essential components is needed to ensure effectiveness; and instill hope for recovery and improve the quality of lives of young people with psychosis and their families.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".