Resumption of work or studies after first‐episode psychosis: the impact of vocational case management
Bibliographic record
Abstract
BACKGROUND: Psychosis compromises the educational and professional projects of young patients. Vocational case management (VCM) offers comprehensive support for reintegration into work or studies within an early psychosis intervention programme. AIMS: To evaluate the effectiveness of VCM in resumption of work or school and to identify the predictive factors of occupational outcome. METHODS: This descriptive study focused on occupational status of an early psychosis cohort during the first 5 years of VCM. RESULTS: 56.6% of 97 study subjects had a diagnosis of schizophrenia, 32% had type I bipolar disorder with psychotic features. 68% held a productive occupation the year prior to admission, and 47.4% at admission. The occupational rate rose from 57.1% at 12 months to over 70% after 48 months. 65.6% maintained or improved their occupational status. Most subjects held competitive employment, and the employment rate was similar to that of the general population. Prior employment and affective psychosis were associated with better outcome. [Correction added on 2 April 2013, after first online publication: 'Non-affective psychosis' has been changed to 'affective psychosis' in the Results section.] CONCLUSION: The majority of individuals suffering from early psychosis resume productive activity rapidly when offered VCM within an early intervention programme during a follow-up period of up to 5 years.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".