Testing the theory of change for Housing First: a secondary qualitative analysis of gender differences in the experiences of men and women in the AH/CS trial
Bibliographic record
Abstract
OBJECTIVES: Housing First (HF) is an evidence-based approach to ending homelessness, particularly for individuals with mental illness. Yet, limited research explores which aspects of HF programmes facilitate change over time, within the context of a programme theory of change (ToC). A particular research gap includes how mechanisms of change within HF programmes differ between men and women. This study examines gender-specific pathways of change in the HF model based on secondary qualitative data from Toronto's original At Home/Chez Soi (AH/CS) trial, focusing on outcomes of housing stability, socio-economic status, health and overall well-being. DESIGN: This was a secondary qualitative analysis of the AH/CS trial data. This analysis was guided using a gender-sensitive ToC framework. SETTINGS: This multisector study was conducted in a large Canadian urban centre in Toronto, Canada. PARTICIPANTS: A total of 32 participants (23 men and nine women) who identified themselves as male or female, 18 months after their enrolment in the treatment arm of the Toronto site of the AH/CS randomised controlled trial. DATA COLLECTION AND ANALYSIS: Semistructured interviews were conducted as part of the trial's qualitative study. Thematic analysis was guided by the ToC framework and conducted using NVivo software. We assessed differences between men and women across the following outcome domains: housing stability, financial status, physical and mental health, substance use recovery and inpatient care. RESULTS: The findings largely confirmed the ToC with participants, particularly women, experiencing greater improvements across all mechanisms of change, especially in housing stability, financial status and health outcomes. Men faced ongoing challenges, including difficulty maintaining stable income, limited engagement with education/training and continued struggles with mental health and substance use. Despite these improvements, both men and women participants reported ongoing challenges in achieving consistent income and accessing education or training opportunities. CONCLUSIONS: This study provides insight into how mechanisms of change within HF programmes differ between men and women. It underscores the need for ongoing programme adaptation and gender-responsive evaluation to meet the diverse needs of individuals, particularly those with mental health illness and histories of chronic homelessness.
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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 | 0.063 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".