Comparing health service use before and after transition to a supported housing model for clients who experience severe and persistent mental illness in southwestern Ontario, Canada
Bibliographic record
Abstract
Objective and ApproachBeginning in 2018, a provincially-funded custodial housing model in Ontario, Canada transitioned to a supported housing model that allowed more autonomy for clients who experience severe and persistent mental illness (SPMI). The objective of this study was to compare health service use before and after transition to the new Community Homes for Opportunity (CHO) program in southwestern Ontario between 2017 and 2019. Information about clients who transitioned to the CHO program were obtained from the Ontario Ministry of Health and Long-Term Care and linked to health administrative data at ICES. Rates of emergency department (ED) visits, primary care visits, and specialist visits in the one year before and after implementation of the CHO program were compared using conditional Poisson models. ResultsOf the 368 clients, approximately 40% were female and the mean age was 57 years old. Compared to before the transition, clients had more primary care visits (rate ratio [RR] = 1.21; 95% CI: 1.07-1.37) and specialist visits (RR = 1.33; 95% CI: 1.11-1.60) within the year after transition to the CHO program. There was no significant change in the use of emergency department visits following the transition (RR = 1.20; 95% CI: 0.96-1.50). ConclusionsSupported housing may be associated with greater use of primary and specialist care for clients who experience SPMI. ImplicationsThe new supported housing model may encourage clients to use more personalized health care, according to their specific needs. Such personalized support is crucial for clients with complex health care needs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".