Generalisability of the individual placement and support model of supported employment: results of a Canadian randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: Studies conducted in the USA have found the individual placement and support model of supported employment to be more effective than traditional vocational rehabilitation at helping people with severe mental illness to find and maintain competitive employment. AIMS: To determine the effectiveness of the individual placement and support (supported employment) model in a Canadian setting. METHOD: A total of 150 adults with severe mental illness, who were not currently employed and who desired competitive employment, were randomly assigned to receive either supported employment (n = 75) or traditional vocational services (n = 75). RESULTS: Over the 12 months of followup, 47% of clients in the supported employment group obtained at least some competitive employment, v. 18% of the control group (P < 0.001). They averaged 126 h of competitive work, v. 72 in the control group (P < 0.001). CONCLUSIONS: Supported employment proved more effective than traditional vocational services in a setting significantly different from settings in the USA, and may therefore be generalised to settings in other countries.
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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 | 0.043 | 0.107 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".