Homeowner perspectives on the implementation of the Community Homes for Opportunity (CHO) program: an ethnographic group homes study in Southwestern Ontario Canada
Bibliographic record
Abstract
BACKGROUND: The global extant literature acknowledge that housing serves as a key social determinant of health. Housing interventions that involve group homes have been found to support the recovery of persons with mental illness and those with addiction issues. The current study explored the views of homeowners in relation to a supportive housing program called Community Homes for Opportunity (CHO) that modernised a provincial group home program (Homes for Special Care [HSC]) and provided recommendations for improving the program implementation in other geographical areas of Ontario. METHODS: We applied ethnographic qualitative techniques to purposefully recruit 36 homeowner participants from 28 group homes in Southwest Ontario, Ontario Canada. Focus group discussions were conducted at two time points, during CHO program implementation (Fall 2018, and post implementation phases (Winter 2019) respectively. RESULTS: Data analysis yielded 5 major themes. These include: (1) general impressions about the modernization process, (2) perceived social, economic and health outcomes, (3) enablers of the modernization program, (4) challenges to implementation of the modernization program, and (5) suggestions for implementation of the CHO in future. CONCLUSIONS: A more effective and expanded CHO program will need the effective collaboration of all stakeholders including homeowners for successful implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".