Hospitalization among street-involved youth who use illicit drugs in Vancouver, Canada: a longitudinal analysis
Bibliographic record
Abstract
BACKGROUND: Street-involved youth who use illicit drugs are at high risk for health-related harms; however, the profile of youth at greatest risk of hospitalization has not been well described. We sought to characterize hospitalization among street-involved youth who use illicit drugs and identify the most frequent medical reasons for hospitalization among this population. METHODS: From January 2005 to May 2016, data were collected from the At-Risk Youth Study (ARYS), a prospective cohort study of street-involved youth in Vancouver, Canada. Multivariable generalized estimating equation (GEE) was used to identify factors associated with hospitalization. RESULTS: Among 1216 participants, 373 (30.7%) individuals reported hospitalization in the previous 6 months at some point during the study period. The top three reported medical reasons for hospital admission were the following: mental illness (37.77%), physical trauma (12.77%), and drug-related issues (12.59%). Factors significantly associated with hospitalization were the following: past diagnosis of a mental illness (adjusted odds ratio [AOR] = 1.85; 95% confidence interval [95% CI] 1.47-2.33), frequent cocaine use (AOR = 2.15; 95% CI 1.37-3.37), non-fatal overdose (AOR = 1.76; 95% CI 1.37-2.25), and homelessness (AOR = 1.40; 95% CI 1.16-1.68) (all p < 0.05). CONCLUSIONS: Findings suggest that mental illness is a key driver of hospitalization among our sample. Comprehensive approaches to mental health and substance use in addition to stable housing offer promising opportunities to decrease hospitalization among this vulnerable 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, 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".