Moving from rhetoric to reality: adapting Housing First for homeless individuals with mental illness from ethno-racial groups
Bibliographic record
Abstract
BACKGROUND: The literature on interventions addressing the intersection of homelessness, mental illness and race is scant. The At Home/Chez Soi research demonstration project is a pragmatic field trial investigating a Housing First intervention for homeless individuals with mental illness in five cities across Canada. A unique focus at the Toronto site has been the development and implementation of a Housing First Ethno-Racial Intensive Case Management (HF ER-ICM) arm of the trial serving 100 homeless individuals with mental illness from ethno-racial groups. The HF ER-ICM program combines the Housing First approach with an anti-racism/anti-oppression framework of practice. This paper presents the findings of an early implementation and fidelity evaluation of the HF ER-ICM program, supplemented by participant narrative interviews to inform our understanding of the HF ER-ICM program theory. METHODS: Descriptive statistics are used to describe HF ER-ICM participant characteristics. Focus group interviews, key informant interviews and fidelity assessments were conducted between November 2010 and January 2011, as part of the program implementation evaluation. In-depth qualitative interviews with HF ER-ICM participants and control group members were conducted between March 2010 and June 2011. All qualitative data were analysed using grounded theory methodology. RESULTS: The target population had complex health and social service needs. The HF ER-ICM program enjoyed a high degree of fidelity to principles of both anti-racism/anti-oppression practice and Housing First and comprehensively addressed the housing, health and sociocultural needs of participants. Program providers reported congruence of these philosophies of practice, and program participants valued the program and its components. CONCLUSIONS: Adapting Housing First with anti-racism/anti-oppression principles offers a promising approach to serving the diverse needs of homeless people from ethno-racial groups and strengthening the service systems developed to support them. The use of fidelity and implementation evaluations can be helpful in supporting successful adaptations of programs and services.
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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".