Generalizability of the Individual Placement and Support (IPS) model of supported employment outside the US
Bibliographic record
Abstract
While reviews of controlled studies of the Individual Placement and Support (IPS) model of supported employment for clients with severe mental illness have documented its effectiveness in the US, its generalizability to other countries has not been systematically evaluated. This is the first review to compare US to non-US studies. We identified 15 randomized controlled trials of IPS programs, 9 in the US and 6 outside the US. We examined competitive employment outcomes, including employment rate, days to first job, weeks worked during follow-up, and hours worked. We also considered noncompetitive employment, program retention, and nonvocational outcomes. IPS programs had significantly better outcomes across a range of competitive employment indicators and higher retention in services than control groups. The overall competitive employment rate for IPS clients in US studies was significantly higher than in non-US studies (62% vs. 47%). The consistently positive competitive employment outcomes strongly favoring IPS over a range of comparison programs in a group of international studies suggest that IPS is an evidence-based practice that may transport well into new settings as long as programs achieve high fidelity to the IPS model, but further research is needed on international adaptations.
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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 | 0.372 | 0.585 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".