Health and social outcomes in the Housing First model: Testing the theory of change
Bibliographic record
Abstract
Background: Homelessness continues to grow globally. The Housing First (HF) model offers immediate access to housing and support services without preconditions and has a growing body of evidence documenting its effectiveness at ending homelessness. HF has a robust theory of change that hypothesizes how unique program components (i.e., immediate access to housing, separation of services from housing, client choice, etc.) drive positive social and health changes over time. We advance the understanding of how HF causes client improvement by empirically testing this program's theory of change. Methods: Using a unique longitudinal quantitative data from the large Canadian At Home/Chez Soi Housing First trial we used path analysis to test the theory of change for Quality of Life, Crisis related events or service utilization, and Recovery. Program pathways and health and social outcomes were measured at enrolment, 6-, 12- and 24-months post-enrolment. Findings: Most hypothesized pathways were confirmed with path analysis. Confirmed pathways for two outcomes- Quality of Life (QOL) and Recovery - were similar. Health and social consultations at enrolment, health status at 6- and 12-months post enrolment, and social connectedness at 12-months were important predictors of the 24-month outcomes of Quality of Life and Recovery, but not for Crisis related events or service utilization. Interpretation: This analysis directly responds to recent calls for more empirical evidence about intervention mechanisms. Ensuring linkages to health and social service consultations for clients, supporting clients' engagement with family and community, and enabling clients to improve or maintain good health will drive better longer term client outcomes within Housing First. Funding: Funding Mental Health Commission of 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 | 0.054 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".