Interventions for stimulant use in people who are homeless or vulnerably housed: a scoping review of the evidence including trauma-informed approaches
Bibliographic record
Abstract
BACKGROUND: Stimulant use has been increasing globally over the past decade. People using drugs are now most often using both opioids and stimulants. In Canada, stimulants were involved in 68% of overdose deaths from 2020 to 2024. The overdose crisis has disproportionately impacted people experiencing homelessness. People experiencing homelessness are more likely to experience an overdose and stimulants are more likely to contribute directly to an overdose death in this population. Despite the growing need, a comprehensive and effective treatment program for people experiencing homelessness who use stimulants, that also considers trauma and safe supply options, is non-existent. We conducted a scoping review to identify the types of evidence-based treatments available to address stimulant use in people experiencing homelessness; whether any of these services were trauma-informed; and to identify knowledge gaps. METHODS: Using a peer-reviewed search strategy, we conducted searches in MEDLINE, Embase, PsychInfo via OVID, CINAHL, Global Health via EBSCOhost, and Scopus. Grey literature sources were hand searched. We included any primary research study with no restrictions on language or date. Reporting follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. RESULTS: After de-duplication, 2914 titles and abstracts were screened, and 51 papers were moved to full text screening. Of this, 31 studies met eligibility criteria and were included for data extraction. Papers examined contingency management interventions (n = 20), residential treatment programs (n = 5), safer supply (n = 1), and other types of interventions (n = 4). Of the 31 papers, no studies reported using trauma-informed care approaches, although, seven incorporated some components of trauma-informed care. There was mixed evidence of effectiveness among studies, with rewards-based contingency management being generally effective and acceptable to participants. DISCUSSION: There is limited evidence available regarding stimulant use treatments for people experiencing homelessness, with most studies focusing on contingency management. Studies largely do not consider the effects of trauma in their intervention or study design. Further research is needed on harm reduction interventions such as stimulant safer supply or supervised consumption services, as well as outcomes other than abstinence that may be important to this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".