Home Staff Perspectives on Implementation of the Community Homes for Opportunity (CHO) Program in Southwestern Ontario
Bibliographic record
Abstract
Introduction: Literature has established a bidirectional relationship between housing stability and mental health. Globally, there is a lack of affordable, safe, and appropriate housing for people with mental illness, and Canada is no exception. The current study explored the views of home staff on their experiences regarding the process of modernizing group homes (Community Homes for Opportunity [CHO]). It provided recommendations for further improvement of the implementation process. Method: We used ethnographic techniques to purposefully recruit 51 home staff from 28 group homes in Southwest Ontario, Canada. Focus group discussions were conducted at two‐time points (baseline: spring 2018 and postimplementation: winter 2019). Results: Data analysis produced four major themes. These include a general impression of the modernization process, facilitators, challenges to the implementation, and suggestions for improving the modernization program. Conclusion: Group homes such as CHO positively impact the well‐being and quality of life of persons with mental health and addiction problems while enhancing their independence and social integration for improved recovery outcomes.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 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".