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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".