Police Documentation of Alcohol Involvement in Hospitalized Injured Drivers
Bibliographic record
Abstract
OBJECTIVE: Injured drivers with blood alcohol concentration (BAC) above the legal limit are rarely convicted of impaired driving. One explanation is that police may have difficulty recognizing alcohol intoxication in injured drivers. In this study, we compare police documentation of alcohol involvement with BAC measured on arrival at a hospital. Our objectives were to determine how often police document alcohol involvement in injured drivers with BAC ≥ 0.05 percent and identify factors that influence police documentation of alcohol involvement. METHODS: We included injured drivers (1999-2003) who were admitted to a British Columbia trauma center or treated in the Vancouver General Hospital emergency department. We used probabilistic linkage to obtain police collision reports. Police were considered to have indicated alcohol involvement if (1) police documented that alcohol contributed to the crash, (2) the driver received an administrative sanction for impaired driving, or (3) the driver was criminally convicted of impaired driving. The proportion of drivers for whom police indicated alcohol involvement was determined relative to age, gender, BAC levels, crash severity, and crash characteristics. Multivariate logistic regression was used to identify factors independently associated with police indication of alcohol involvement. RESULTS: Two thousand four hundred and ten injured drivers (73.5% male) were matched to a police report. Overall, 857 (35.6%) drivers tested positive for alcohol (BAC ≥ 0) and 736/857 (85.9%) of alcohol-positive drivers had a BAC ≥ 0.05 percent (the legal limit in British Columbia). Of the 736 drivers with a BAC > 0.05 percent at time of admission, police indicated alcohol involvement in 530 (72.0%). The criminal code conviction rate for impaired driving was 4.7 percent for drivers with 0.08 percent ≤ BAC < 0.16 percent and 13.6 percent for drivers with BAC > 0.16 percent. The following factors were associated with higher odds of police indicating alcohol involvement: (1) increasing blood alcohol levels, (2) a prior record of impaired driving, (3) involvement in a single-vehicle crash, (4) involvement in a nighttime crash, and (5) traffic violations or unsafe driving actions recorded by police. CONCLUSIONS: Police recognized and documented alcohol involvement in 72 percent of injured drivers with BAC ≥ 0.05 percent. Police documentation of alcohol involvement was more common at higher BAC levels, in nighttime or single-vehicle crashes, for drivers who committed traffic violations or drove unsafely, and for drivers with a prior record of impaired driving. The low conviction rate of injured impaired drivers does not appear to be due to police inability to recognize alcohol involvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".