Housing first and emergency department utilization among homeless individuals with mental illness in Vancouver
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate the impact of Housing First at 12 months on emergency department (ED) use among homeless individuals with mental illness in Vancouver. METHODS: Homeless individuals with mental illness enrolled in the At Home/Chez Soi study were randomly assigned to a Housing First model of housing and support or Usual Care. ED data from St. Paul’s Hospital and Mount Saint Joseph Hospital were linked for 382 participants in Vancouver. Negative binomial regression was used to compare the Rate of Visits and logistic regression was used to compare the Presenting Complaints, Discharge Diagnosis, and Disposition for visits from participants in Housing First or Usual Care. RESULTS: 60% of all participants visited the ED at least once at 12 months and the annual Rate of Visits was 3.32 (SD=6.21). After adjusting for the baseline rate and other potential confounders, the Rate of Visits at 12 months was 0.66 times less (95% CI: 0.47-0.93) for Housing First participants compared to Usual Care. For all participants at 12 months, mental health and substance use-related reasons accounted for 30% of Presenting Complaints and 34% of Discharge Diagnoses. The majority of visits (87%) resulted in discharge from the ED, including 9% left against medical advice or without being seen, and 13% resulted in care advance. For Housing First participants compared to Usual Care at 12 months, the odds ratio for a mental health and substance use-related Presenting Complaint (OR=0.98, 95% CI: 0.64-1.51) or Discharge Diagnosis (OR=1.18, 95% CI: 0.75-1.86) was not statistically significant, nor was the odds ratio for a care advance Disposition (OR=1.21, 95% CI: 0.68-2.15). CONCLUSION: This study observed lower ED utilization for Housing First, consistent with findings from previous evaluations in other settings, which suggests that this model should be expanded for homeless individuals with mental illness similar to those recruited in this study. This study also observed that many participants experienced acute mental health issues resulting in visits to the ED, and visited the ED without receiving care, which suggests there are opportunities to improve care for this population in the ED and other settings.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".