A review of housing-first strategies in reducing rates of substance use amongst people experiencing homelessness
Bibliographic record
Abstract
Substance use is a serious and prevalent health challenge among people who are homeless. The rate of alcohol use is 6 to 7 times more common in people experiencing homelessness than the general population, while approximately a quarter of all homeless individuals exhibit disordered use of one or more substances other than alcohol. Substance use is recognized as a significant risk factor for becoming homeless and is thought to complicate rehousing efforts and contribute to decreased adherence to rehabilitation programs that were and continue to be traditionally a requirement of rehousing. While studies have largely shown that housing-first strategies result in increased rates of retention in permanent housing compared to more established treatment-first strategies, it is less established whether housing-first strategies are equally successful among those homeless individuals with substance use challenges. In this review, we examine the available evidence on the efficacy of housing-first strategies in rehousing individuals with substance use challenges and in reducing the rates of substance use among people experiencing homelessness. We conclude that while housing-first strategies have not been shown to reduce rates of substance use compared to treatment-first strategies, both types of programs result in a comparable level of decrease in substance use rates despite treatment-first strategies mandating rehabilitation prior to rehousing. Finally, we provide a number of guidelines for an interdisciplinary approach to rehousing homeless individuals with substance use disorders through a housing-first strategy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".