Collaborative Mental Health Care for the Homeless: The Role of Psychiatry in Positive Housing and Mental Health Outcomes
Bibliographic record
Abstract
OBJECTIVE: Factors associated with positive outcomes for homeless men referred to a shelter-based collaborative mental health care team were examined. METHOD: A chart review of 73 clients referred over 12 months was completed. Two outcome measures were examined, clinical status and housing status, 6 months after their referral to the program. RESULT: Among the referred clients, the prevalence of severe and persistent mental illness and substance use disorders was 76.5% and 48.5%, respectively. At 6 months, 24 clients (35.3%) had improved clinically, and 33 (48.5%) were housed. Logistic regression identified 2 factors associated with clinical improvement: the number of visits with a psychiatrist and treatment adherence. The same 2 factors were associated with higher odds of housing, and presence of substance use disorder was associated with lower odds of housing at 6-month follow-up. CONCLUSION: Care by a mental health specialist is positively associated with improved outcomes. Strategies to improve treatment adherence, access to mental health specialists, and innovative approaches to treatment of substance use disorders should be considered for this population. Having a psychiatrist as a member of a shelter-based collaborative care team is one possible way of addressing the complex physical and mental health needs of homeless individuals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".