High-intensity cannabis use and hospitalization: a prospective cohort study of street-involved youth in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: There is concern that cannabis use negatively affects vulnerable groups such as youth; however, the relationship between cannabis use and health care utilization has not been well characterized in this population. We longitudinally evaluated the association between daily cannabis use and hospitalization among a prospective cohort of street-involved youth. METHODS: Data were collected from the At-Risk Youth Study (ARYS) in Vancouver, Canada, from September 2005 to May 2015. Participants were interviewed semi-annually and multivariable generalized estimating equation (GEE) logistic regression was used to examine the relationship between daily cannabis use and hospitalization. RESULTS: A total of 1216 participants (31.2% female) were included in this analysis, and 373 (30.7%) individuals reported hospitalization at some point during the study period. In a multivariable GEE analysis, daily cannabis use was not significantly associated with hospitalization (Adjusted Odds Ratio [AOR] = 1.17, 95% Confidence interval [CI] = 0.84, 1.65). We did observe a significant interaction between daily cannabis use and sex (AOR = 0.51, 95% CI = 0.34, 0.77), whereby cannabis use was associated with a decreased odds of hospitalization among males (AOR = 0.60, 95% CI = 0.47, 0.78), yet was not significantly associated with hospitalization among females (AOR = 1.19, 95% CI = 0.84, 1.67). CONCLUSIONS: The finding that daily cannabis use was not associated with hospitalization among street-involved youth is encouraging given the high rates of cannabis use in this population and the expansion of cannabis legalization and regulation. Future studies, however, are warranted to monitor possible changes in the consequences of cannabis use as cannabis legalization and regulation increase internationally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".