Examining age- and sex-related risk factors in first-time DWI offenders
Bibliographic record
Abstract
DWI research has focused on younger males, consequently much less is known about female and older offenders. Quebec data demonstrate a bimodal distribution in first-time driving while impaired (fDWI) offences, a pattern also observed internationally. Malesr fDWI offences peak between the ages of 18 to 25 with a second smaller peak between 35 to 44. In contrast, females' fDWI offences peak between 36 to 47 with a smaller peak between 20 to 27. These observations suggest that the trajectory to fDWI offending is influenced by interactions between sex and age. This preliminary study explores how sex and age may interact to influence the trajectory to fDWI through patterning of alcohol consumption, psychological adjustment, and impulsivity. Individuals arrested for a fDWI within the past two years were recruited. Participants were administered the AUDIT, Timeline Follow Back, MCMI, UPPS, and Stoplight and Balloon Analog Risk Tasks. Thirty-six participants were recruited: younger females, younger males, older males, older females. Younger females engaged in more binge drinking, endorsed greater symptoms of alcohol misuse, and scored higher on urgency than both younger and older males. No significant findings were observed between older females and all other groups. fDWI offending among younger females appear to be related to binge drinking and emotion-related impulsivity. Younger females appear to be different than male offenders of all ages. The findings suggest that female fDWI is not analogous to male fDWI, a postulate that may need to be considered for intervention/prevention efforts with female fDWI offenders.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".