Cost-effectiveness of the individual placement and support model of supported employment for people with severe mental illness: results from a Canadian randomized trial
Bibliographic record
Abstract
Background Several studies have shown that the Individual Placement and Support (IPS) model of supported employment is an effective approach to help many people with severe mental illness to find and maintain competitive employment. These studies include a randomized trial conducted in Montreal, Canada. Very few studies, however, have evaluated the cost-effectiveness of IPS compared to traditional services. Objective To evaluate the cost-effectiveness of IPS model compared to usual vocational services, using data from the Montreal trial. Methods A total of 149 unemployed adult with severe mental illness were randomly assigned to receive either IPS or usual vocational services and were followed for 12 months. Costs were estimated from the perspectives of the health and social care system, the government, and the society. Competitive employment hours and wages were taken as measures of effectiveness. A cost-effectiveness analysis was conducted, using the net benefit framework. A sensitivity analysis was carried out to take into account baseline differences of inpatient days between the two groups. Results IPS dominated usual services with significantly better competitive employment outcomes and less average costs, regardless of the economic perspective. IPS is likely to be more cost-effective than usual services even if the decision maker is only willing to pay a small amount of money per unit improvement in employment outcomes. If only clients without inpatient days during the year before the baseline are considered, IPS costs more than usual services but still with significantly better competitive employment outcomes: $155.73 per additional competitive employment hour and $20.12 per additional dollar of competitive employment earnings from the health and social care services perspective. Conclusions In this study, IPS proved cost-effective compared to usual services, although the baseline difference in inpatient days attenuates the strength of this finding.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".