Passengers at risk: a multi‐level analysis of the decision to travel with a drunk driver
Bibliographic record
Abstract
AIMS: To assess the impact of demographic, intrapersonal and environmental factors on the likelihood in Chile of becoming a passenger of an alcohol-impaired driver (PAID). DESIGN: Multi-level cross-sectional study. SETTING: Data were acquired from two large-scale household surveys of representative samples of Chile's Metropolitan Region. PARTICIPANTS: The study included 1341 individuals ≥ 16 years; 696 of them reported engaging in PAID. MEASUREMENT: The primary outcome was self-reported frequencies of having accepted a ride with an alcohol-impaired driver. FINDINGS: PAID was associated significantly with 'age' [odds ratio (OR) = 0.99, 95% confidence interval (CI) = 0.98-0.99, P = 0.03], 'traffic safety beliefs' (OR = 0.17, 95% CI = 0.078-0.44, P = 0.00), 'time exposure as passenger' (OR = 1.00, 95% CI = 1.00-1.01, P = 0.00) and 'sex' (OR = 0.67, 95% CI = 0.53-0.87, P = 0.00). In women, PAID was associated with 'age' (OR = 0.98, 95% CI = 0.97-0.99, P = 0.02), 'traffic safety beliefs' (OR = 0.17, 95% CI = 0.05-0.59, P = 0.00) and 'low quality of public bus stops' (OR = 0.97, 95% CI = 0.98-0.99, P = 0.01). Younger women with high education levels were more likely to engage in PAID than younger women with low education levels. In men, PAID was associated with 'traffic safety beliefs' (OR = 0.13, 95% CI = 0.02-0.63, P = 0.01) and 'time exposure as passenger' (OR = 1.00, 95% CI = 1.00-1.01, P = 0.02). CONCLUSIONS: In Chile, men are at higher risk of becoming a passenger of an alcohol-impaired driver (PAID) than women. In women, PAID appears to be associated with lower traffic safety beliefs, lower quality of public bus stations and an interaction between education and age. In men, PAID is associated with lower traffic safety beliefs and higher time exposure as passenger.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".