Bibliographic record
Abstract
This study explored the current state of alcohol-impaired driving as well as the changes in alcohol-impaired driving over time among Albertans. Based on self-report data from the annual Alberta Surveys 1991, 1992, 1997, and 2009, this study also traced the shift in the impact of standard demographic factors on alcohol-impaired driving in the province. Furthermore, the study examined social influence in alcohol-impaired driving in a representative sample in Alberta. Results indicated that in the past 12 months, 4% of the respondents had driven a vehicle while impaired, and 6.1% of the respondents had been passengers in a vehicle driven by an impaired driver. Chi-square test indicated that male, single, employed, non-religious, and younger respondents were more likely to have driven while impaired. Logistic regression analyses showed that a one-unit increase in social influence was associated with 5.32 times greater odds of engaging in impaired driving (OR = 5.32, 95% CI = 3.06–9.24, p < .001), controlling for other variables in the model. Findings also showed that self-reported alcohol-impaired driving has decreased substantially over the years (10.6% in 1991, 8.4% in 1992, 7.2% in 1997, and 3.7% in 2009). However, there had been little changes in designated driving. In addition, there had been a shift in age-related impaired driving, i.e., people aged 55-65+ reported impaired driving more in 2009 (4.8%) compared to 1991 (2.0%) and 1992 (2.2%); while individuals aged 18-34 and 35-54 reported impaired driving less in 2009 (4.8% and 2.6%, respectively) compared to 1991 (12.7% and 13.0%, respectively). The policy implications of the findings are discussed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".