Housing-Based Support Models for Individuals with Substance Use and Concurrent Disorders: A Rapid Review
Bibliographic record
Abstract
This rapid review protocol outlines a systematic approach to synthesize evidence on housing and support models for individuals experiencing homelessness with substance use and concurrent disorders (SUCDs). The review aims to identify key components of interventions (e.g., Housing First, harm reduction strategies) and evaluate their effectiveness in improving housing stability, quality of life, health care utilisation and substance use outcomes. A comprehensive search will be conducted across databases including PubMed, Web of Science, and Medline, focusing on peer-reviewed English-language studies published between 2000 and 2025. Eligible studies must include empirical data on SUCD populations, with independent reviewers conducting two-stage screening (title/abstract and full-text) and standardised data extraction. Study quality will be assessed using the Cochrane Risk of Bias tool for randomised trials and the Newcastle-Ottawa Scale for observational studies. Findings will be synthesised narratively, supported by tabular summaries of intervention characteristics and outcomes. By addressing gaps in understanding tailored approaches for this vulnerable population, the review seeks to inform policies and practices aimed at enhancing housing stability, reducing healthcare disparities, and supporting recovery in homeless populations with complex needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".