Effects of Housing First on Employment and Income of Homeless Individuals: Results of a Randomized Trial
Bibliographic record
Abstract
OBJECTIVE: Housing First is emerging as an evidence-based practice for housing and supporting people who are homeless and have a mental illness. The objective of this study was to determine whether Housing First increases the odds of obtaining competitive employment in this population and affects income, including income from informal and illegal sources. METHODS: A total of 2,148 people with a mental illness were recruited from five Canadian cities while they were homeless, classified as having moderate or high needs, and randomly assigned to Housing First or usual care. Housing First participants with high needs received assertive community treatment (ACT), and those with moderate needs received intensive case management (ICM). Every three months, participants were interviewed about employment and earnings in the previous months (median follow-up=745 days). Regression models were estimated via generalized estimating equations. RESULTS: ICM recipients had lower odds of obtaining employment compared with the control group with moderate needs. The odds of obtaining employment among ICM recipients increased but their employment rate never exceeded that of the control group. For ACT recipients, the odds of obtaining employment were not significantly different from those of the control group. Among Housing First participants, persons employed at baseline, men, and younger participants had greater odds of employment compared with control participants. Housing First did not appear to significantly increase income. CONCLUSIONS: This was the first large-scale randomized controlled study of Housing First's effects on employment. Further research is needed to determine how Housing First may be enhanced to increase odds of obtaining employment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".