Managing high-risk behaviours and challenges to prevent housing loss in permanent supportive housing: a rapid review
Bibliographic record
Abstract
Permanent supportive housing is an effective intervention for stably housing most people experiencing homelessness and mental illness who have complex support needs. However, high-risk behaviours and challenges are prevalent among this population and have the potential to seriously harm health and threaten housing tenures. Yet, the research on the relationship between high-risk issues and housing stability in permanent supportive housing has not been previously synthesized. This rapid review aimed to identify the housing-related outcomes of high-risk behaviours and challenges in permanent supportive housing settings, as well as the approaches used by agencies and residents to address them. A range of high-risk behaviours and challenges were examined, including risks to self (overdose, suicide/suicide attempts, non-suicidal self-injury, falls/fall-related injuries), and risks to multiple parties and/or building (fire-setting/arson, hoarding, apartment takeovers, physical/sexual violence, property damage, drug selling, sex trafficking). The search strategy included four components to identify relevant academic and grey literature: (1) searches of MEDLINE, APA PsycINFO, and CINAHL Plus; (2) hand searches of three journals with aims specific to housing and homelessness; (3) website browsing/searching of seven homelessness, supportive housing, and mental health agencies and networks; and (4) Advanced Google searches. A total of 32 articles were eligible and included in the review. Six studies examined the impacts of high-risk behaviours and challenges on housing tenancies, with overdose being identified as a notable cause of death. Twenty-six studies examined approaches and barriers to managing high-risk behaviours and challenges in PSH programs. These were categorized into eight types of approaches: (1) clinical, (2) relational/educational, (3) surveillant, (4) restrictive, (5) strategic, (6) design-based, (7) legal, and (8) self-defence. Consistent across all approaches was a lack of rigorous examination of their effectiveness. Further, some approaches that are legal, restrictive, surveillant, or strategic in nature may be used to promote safety, but may conflict with other program objectives, including housing stability, or resident empowerment and choice. Research priorities were identified to address the key evidence gaps and move toward best practices for preventing and managing high-risk behaviours and challenges in permanent supportive housing.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".