“Individual Placement and Support” boosts employment for early psychosis clients, even when baseline rates are high
Bibliographic record
Abstract
AIM: Individual Placement and Support is an effective vocational intervention for increasing competitive employment for people with severe mental illness. Little is known, however, about its effectiveness in the context of early psychosis. This study assesses improvements in clients' employment in a phase of illness during which functional abilities often decline. METHODS: The trial design is an assessor-blinded randomized clinical trial, set in the context of a population-based Early Psychosis Intervention program in British Columbia, Canada. Participants were randomized either to 1 year of employment support added to treatment-as-usual, or the latter alone. Interviews at intake captured data regarding demographics, symptom severity, and employment; assessments at 6 and 12 months repeated queries about employment activities. RESULTS: A total of 109 clients were recruited. Employment rates in the Individual Placement and Support group increased over time, unlike the control group. Further, the number of days worked over the 12-month intervention period, compared to the 6 months prior to the study, improved for both groups, but the increase was greater among clients receiving IPS. Sensitivity analysis indicated the advantage in days worked was evident in the second half of the intervention period (6-12 months), but not the first half. CONCLUSIONS: Employment rates, for younger clients in both early-psychosis groups, were high compared to older clients in later stages of illness. In this study, use of the Individual Placement and Support strategy further increased employment, despite the high baseline rates. Further research is needed to identify the optimal timing of employment support for these clients.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".