Factors Associated with High-Frequency Cannabis Use and Driving among a Multi-site Sample of University Students in Ontario
Bibliographic record
Abstract
Cannabis use and driving (CUD) is a growing public health concern. This study’s main objective was to identify distinguishing characteristics associated with high-frequency CUD (HFCUD) activity (i.e., CUD > 12 times) in a multi-site sample of university students who had self-identified as having driven a car within 4 hours of cannabis use in the past year. Participants for the study (n = 248; age 18–28 years) were recruited by mass advertising at five universities in Ontario. Participants were screened for eligibility and assessed by an anonymous interview between April 2005 and March 2006. Bivariate analyses determined factors associated with HFCUD (i.e., > 12 times) vs. a low frequency of CUD (LFCUD); significant factors were subsequently entered into a discriminant function analysis model. HFCUD was associated with several variables, including frequent (i.e., at least weekly) cannabis use; daily driving; perception of own ability to drive not being impaired by cannabis use; and expectation of CUD in the next 12 months (all p < 0.0001). CUD among young drivers is an important health and safety risk requiring effective interventions. Given the strong association of HFCUD with frequent cannabis use, these phenomena need to be addressed conjointly. Furthermore, preventive interventions responsive to the specific socio-cultural contexts of possible CUD need to be developed and implemented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".