First-Episode Psychosis, Early Intervention, and Outcome: What Have We Learned?
Bibliographic record
Abstract
OBJECTIVE: There has been increased interest in the potential of early intervention to positively influence outcome in first-episode psychosis (FEP) and, consequently, to influence mental health policy and practice. This study's objective was to examine the concept of early intervention and the evidence that currently exists to support such a shift in the delivery of care. METHOD: We examined the evidence for phase-specific treatment of FEP, looking for interventions that attempt to arrest the transition from a putative prodromal state to full psychosis, as well as for interventions that attempt to reduce delay in treatment. RESULTS: Some evidence supports specialized FEP interventions for short-term outcome in terms of symptom reduction, relapse rates, and greater adherence to and retention in treatment. As well, there is modest support for benefits to aspects of social and community functioning and satisfaction with life. Limited evidence supports a positive effect of community-wide case detection in terms of reduced delays in treatment and pharmacologic and psychological interventions in the prodromal phase. CONCLUSIONS: The field of early intervention in psychosis is young, with encouraging preliminary results, especially for improving outcome in established FEP. It requires further study, especially of longer-term outcome. Further studies need to examine the effects of a specialized approach on longer-term outcome and to explore cost-effective methods to reduce delays in treatment and provide interventions in the prodromal phase.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".