Passengers' Decisions to Ride With a Driver Under the Influence of Either Alcohol or Cannabis
Bibliographic record
Abstract
OBJECTIVE: The purpose of the present study was to identify the risk factors associated with passenger decisions to ride with a driver who is under the influence of either alcohol or cannabis. METHOD: We analyzed data from the 2008 Canadian Alcohol and Drug Use Monitoring Survey (CADUMS), a nationally represented telephone sample of 16,672 Canadians age 15 and older, of whom 60.5% were female. Logistic regression analyses explored the effects of sociodemographic, substance use, and driving-behavior factors on the risk of riding with a drinking driver (RWDD) and riding with a cannabis-impaired driver (RWCD). RESULTS: Risk factors for RWDD and RWCD were both shared and unique. Common risk factors were respondents' age, with young people at increased risk and those 65 years and older at decreased risk, and problematic alcohol use (as measured by Alcohol Use Disorder Identification Test subscales). Having previously driven under the influence of alcohol increased the risk of RWDD, while RWCD was associated with having previously driven under the influence of cannabis. CONCLUSIONS: Considerable legal and public health attention has been devoted to eliminating impaired driving, with particular focus on driver behavior. However, with the knowledge that impaired driving is strongly related to being a passenger of an impaired driver, prevention efforts to reduce the prevalence of impaired driving must be multifaceted, targeting passengers as well as drivers. Links between attitudes, beliefs, risk-taking behavior, and related structural conditions should be emphasized, with passengers being encouraged to recognize impairment in others and make sensible choices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".