Cost-Effectiveness of Services for Mentally Ill Homeless People: The Application of Research to Policy and Practice
Bibliographic record
Abstract
OBJECTIVE: About one-quarter of homeless Americans have serious mental illnesses. This review synthesizes research findings on the cost-effectiveness of services for this population and their relevance for policy and practice. METHOD: Service interventions for seriously mentally ill homeless people were grouped into three overlapping categories: 1) outreach, 2) case management, and 3) housing placement and transition to mainstream services. Data were reviewed both from experimental studies with high internal validity and from observational studies, which better reflect typical community practice. RESULTS: In most studies, specialized interventions are associated with significantly improved outcomes, most consistently in the housing domain, but also in mental health status and quality of life. These programs are also associated with increased use of many types of health service and housing assistance, resulting in increased costs in most cases. The value of these programs to the public thus depends on whether their greater effectiveness is deemed to be worth their additional cost. CONCLUSIONS: Innovative programs for seriously mentally ill homeless people are effective and are also likely to increase costs in many cases. Their value ultimately depends on the moral and political value society places on caring for its least-well-off members.
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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 | 0.016 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".