Housing First and harm reduction: a rapid review and document analysis of the US and Canadian open-access literature
Bibliographic record
Abstract
Housing First is an evidence-based practice intended to serve chronically homeless individuals with co-occurring serious mental illness and substance use disorders. Despite housing active substance users, harm reduction is an often-overlooked element during the Housing First implementation process in real-world settings. In this paper, we explore the representation of the Housing First model within the open-access scholarly literature as a potential contributing factor for this oversight. We conducted a rapid review of the US and Canadian open-access Housing First literature. We followed a document analysis approach, to form an interpretation of the articles’ content related to our primary research questions. A total of 55 articles on Housing First were included in the final analysis. Only 21 of these articles (38.1%) included explicit mention of harm reduction. Of the 34 articles that did not discuss harm reduction, 22 provided a description of the Housing First model indicating it does not require abstinence from substance use; however, descriptions did not all clearly indicate abstinence was not required beyond program entry. Additional Housing First descriptions focused on the low-barrier entry criteria and/or the intervention’s client-centeredness. Our review demonstrated a lack of both explicit mention and informed discussion of harm reduction in the Housing First literature, which is likely contributing to the Housing First research-practice gap to some degree. Future Housing First literature should accurately explain the role of harm reduction when discussing it in the context of Housing First programming, and public agencies promoting Housing First uptake should provide resources for proper implementation and monitor program fidelity to prevent model drift.
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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 | 0.041 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.070 | 0.067 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".