Cost Analysis of a High Support Housing Initiative for Persons with Severe Mental Illness and Long-Term Psychiatric Hospitalization
Bibliographic record
Abstract
OBJECTIVE: The objective of this article was to conduct a cost analysis comparing the costs of a supportive housing intervention to inpatient care for clients with severe mental illness who were designated alternative-level care while inpatient at the Centre for Addiction and Mental Health in Toronto. The intervention, called the High Support Housing Initiative, was implemented in 2013 through a collaboration between 15 agencies in the Toronto area. METHOD: The perspective of this cost analysis was that of the Ontario Ministry of Health and Long-Term Care. We compared the cost of inpatient mental health care to high-support housing. Cost data were derived from a variety of sources, including health administrative data, expenditures reported by housing providers, and document analysis. RESULTS: The High Support Housing Initiative was cost saving relative to inpatient care. The average cost savings per diem were between $140 and $160. This amounts to an annual cost savings of approximately $51,000 to $58,000. When tested through sensitivity analysis, the intervention remained cost saving in most scenarios; however, the result was highly sensitive to health system costs for clients of the High Support Housing Initiative program. CONCLUSIONS: This study suggests the High Support Housing Initiative is potentially cost saving relative to inpatient hospitalization at the Centre for Addiction and Mental Health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".