Supported housing programs for persons with serious mental illness in rural northern communities: A mixed method evaluation
Bibliographic record
Abstract
BACKGROUND: During the past two decades, consumers, providers and policy makers have recognized the role of supported housing intervention for persons diagnosed with serious mental illness (SMI) to be able to live independently in the community. Much of supported housing research to date, however, has been conducted in large urban centers rather than northern and rural communities. Northern conditional and contextual issues such as rural poverty, lack of accessible mental health services, small or non-existing housing markets, lack of a continuum of support or housing services, and in some communities, a poor quality of housing challenge the viability of effective supported housing services. The current research proposal aims to describe and evaluate the processes and outcomes of supported housing programs for persons living with SMI in northern and rural communities from the perspective of clients, their families, and community providers. METHODS: This research will use a mixed method design guided by participatory action research. The study will be conducted over two years, in four stages. Stage I will involve setting up the research in each of the four northern sites. In Stage II a descriptive cross-sectional survey will be used to obtain information about the three client outcomes: housing history, quality of life and housing preference. In Stage III two participatory action strategies, focus groups and photo-voice, will be used to explore perceptions of supported housing services. In the last stage findings from the study will be re-presented to the participants, as well as other key community individuals in order to translate them into policy. CONCLUSION: Supported housing intervention is a core feature of mental health care, and it requires evaluation. The lack of research in northern and rural SMI populations heightens the relevance of research findings for health service planning. The inclusion of multiple stakeholder groups, using a variety of data collection approaches, contributes to a comprehensive, systems-level examination of supported housing in smaller communities. It is anticipated that the study's findings will not only have utility across Ontario, but also Canada.
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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 | 0.028 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".