Findings From a Process Evaluation of an Indigenous Holistic Housing Support and Mental Health Case Management Program in Downtown Toronto
Bibliographic record
Abstract
While urban Indigenous populations in Canada are increasing and represent many diverse and culturally vibrant communities, disparities between Indigenous and non-Indigenous people’s experiences of the social determinants of health are significant. The Mino Kaanjigoowin (MK) program at Na-Me-Res (Native Men’s Residence) in Toronto, Ontario, Canada, supports Indigenous men who are experiencing homelessness or are precariously housed and who have complex health and social needs. Using a community-partnered approach that aligns with wise practices for conducting Indigenous health research, a mixed-methods process evaluation of the MK program was conducted in 2017‒2018 by the Well Living House in partnership with Na-Me-Res. Thematic analysis of qualitative data gathered through two focus groups with community members who access the MK program (n = 9) and key informant interviews with staff (n = 11) was carried out using a decolonizing lens. Results indicate that the MK program provides a unique healing model that is grounded in trust, honour, and respect. Strengths of the program include a harm reduction framework, meeting basic needs, and person-centred care. The program could be enhanced through increased human resource capacity and improved infrastructure, including a separate space for MK staff and activities. The evaluation findings demonstrate how the MK program provides specialized and culturally safe services as a best- practice model to meet the complex health and social needs of urban Indigenous people.
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".