Young Adults’ Perspectives on Factors Related to Relapse After First-Episode Psychosis: Qualitative Focus Group Study
Bibliographic record
Abstract
Relapse after first-episode psychosis (FEP) is a major clinical challenge for specialized early intervention services. Understanding patient perspectives on factors contributing to relapse can inform the development of risk assessments and preventive interventions. The objective of this study was to identify factors that may contribute to and prevent relapse from the perspectives of patients receiving services for FEP. Data from 25 participants across four focus groups in Canada were analyzed with a descriptive content analysis approach. Twelve factors were identified, of which four (social environment, technology use, medication, and lifestyle behaviors) had both contributory and preventive roles. In descending order of frequency, risk factors for relapse included substance use; unsupportive social environment; technology use; taking and not taking medication; lack of sleep; work, career, or school stress; significant life events; symptoms of depression or mania; generalized worry; and financial stress. Preventive factors consisted of having a supportive social environment, using technology, taking medication, using coping strategies, and engaging in healthy lifestyle behaviors and meaningful activities. These findings extend the literature on relapse vulnerability and protective factors. Importantly, the factors identified in this study are modifiable, and thereby provide insights for the development and optimization of relapse risk assessments and preventive interventions.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".