Community-based services for homeless adults experiencing concurrent mental health and substance use disorders: a realist approach to synthesizing evidence
Bibliographic record
Abstract
Consultations with community-based service providers in Toronto identified a lack of strong research evidence about successful community-based interventions that address the needs of homeless clients experiencing concurrent mental health and substance use disorders. We undertook a collaborative research effort between academic-based and community-based partners to conduct a systematic evidence synthesis drawing heavily from Pawson's realist review methodology to focus on both whether programs are successful and why and how they lead to improved outcomes. We examined scholarly and nonscholarly literature to explore program approaches and program elements that lead to improvements in mental health and substance use disorders among homeless individuals with concurrent disorders (CD). Information related to program contexts, elements, and successes and failures were extracted and further supplemented by key informant interviews and author communication regarding reviewed published studies. From the ten programs that we reviewed, we identified six important and promising program strategies that reduce mental health and, to a far lesser degree, substance use problems: client choice in treatment decision-making, positive interpersonal relationships between client and provider, assertive community treatment approaches, providing supportive housing, providing supports for instrumental needs, and nonrestrictive program approaches. These promising program strategies function, in part, by promoting and supporting autonomy among homeless adults experiencing CD. Our realist informed review is a useful methodology for synthesizing complex programming information on community-based interventions.
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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 | 0.149 | 0.339 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.038 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".