Economic Impacts of Supported Employment for Persons with Severe Mental Illness
Bibliographic record
Abstract
BACKGROUND: Most persons with severe mental illness prefer competitive to sheltered vocational settings. Supported employment (SE) has become a clearly defined model for helping people with severe mental illness to find and maintain competitive jobs. It involves individualized and rapid placement, ongoing support and assessment, and integration of vocational and mental health staff within a single clinical team. Previous studies show that SE secures competitive employment much more effectively than do other approaches. This review focuses on its economic impacts. METHODS: Studies reporting some service use or monetary outcomes of adding SE programs were identified. These outcomes were tabulated and are discussed in narrative form. RESULTS: Five nonrandomized and 3 randomized studies compare SE programs with day treatment or transitional employment programs. The introduction of SE services can result in anything from an increase to a decrease in vocational service costs, depending on the extent to which they substitute for previous vocational or day treatment services. Overall service costs tend to be lower, but differences are not significant. Earnings increase only slightly on average. CONCLUSIONS: Converting day treatment or other less effective vocational programs into SE programs can be cost-saving or cost-neutral from the hospital, community centre, and government points of view. Investments of new money into SE programs are unlikely to be materially offset by reductions in other health care costs, by reductions in government benefit payments, or by increased tax revenues. Such investments must be motivated by the value of increasing the community integration of persons with severe mental illness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".