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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".