Risk-Taking, Social Support, and Belongingness Contribute to the Risk for Cannabis Use Frequency in University Students
Bibliographic record
Abstract
Background: Cannabis use and misuse is known to be associated with a variety of negative health, academic, and work-related outcomes; therefore, it is important to study the factors that contribute to or moderate its use. Objectives: The aim of this study was to determine whether risky behavior, belongingness and social support as clustering variables play a role in cannabis use frequency. Method: In a university student sample, participant data on risky behavior, belongingness and social support were used to generate vulnerability profiles through cluster analysis (low vulnerability with low risk, low vulnerability with high belonging, moderate vulnerability, and high vulnerability). Using an analysis of variance, the vulnerability profiles were compared with respect to cannabis use frequency and quantity. Through chi-square tests we assessed whether these profiles are overrepresented in certain demographics. Results: The cluster analysis yielded four groups, which differed in their vulnerability for cannabis use. The most vulnerable cluster group had higher cannabis use frequency relative to the two least vulnerable groups. Low income vs. high income was also associated with high vulnerability group membership. International students were overrepresented in the low vulnerability with high belonging group relative to the low vulnerability with low-risk group. The opposite was observed for domestic students. Conclusions: This research adds to the expanding body of literature on cannabis use and misuse in Canada, which may contribute to public health policy and the prevention and treatment of cannabis addiction by providing new insight on who may be at risk.
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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 | 0.001 |
| 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".