Predictors of Frequent Withdrawal Management Unit Use among Chronically Homeless, Homeless, and Housed Men: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Reports suggest that repeat users of detoxification services are less likely to get rehabilitated. The goal of this study is to determine rates and predictors of detoxification unit visits among individuals who are chronically homeless with severe drinking problems compared to those who are housed and in the general homeless population. METHODS: Visit records (n = 1027) from all inner city Toronto detoxification units (n = 5) by men (n = 169) over a 6 year period were analyzed and linked to structured interview data for three populations: chronically homeless individuals with severe drinking problems (CHDP, n = 50); members of the general homeless population (GH, n = 61); and low-income housed individuals (LIH, n = 58). RESULTS: The CHDP group had 4.13 (3.86, 4.39) detoxification unit admissions per year, 18.1 (95% CI 12.5-23.7) and 33 (95% CI 21-46) times higher than the GH and LIH groups respectively. Admission rates were 43.8 % (95% CI 32.7-54.9%) higher in the winter than summer months for the CHDP group. The proportions of stays that involved police, leaving without discharge, and staying two days or less were 74%, 75%, and 89% among CHDP, GH, and LIH subjects. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Rather than being a resource for achieving abstinence, frequent short visits, treatment non-compliance, higher winter visit rates suggest that detoxification units are more likely used by individuals as shelter; high rates of admission related police involvement suggest that they continue to be used as an alternative to judicial intervention into public inebriation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".