PREDICTORS OF FAILED INTERLOCK BAC TESTS AND USING FAILED BAC TESTS TO PREDICT POST-INTERLOCK REPEAT DUIS
Bibliographic record
Abstract
Since 1995, DUI offenders assigned to the interlock have been under study to assess the impact of a four-part program of supportive guidance in Alberta, Canada. The program combines motivational interviewing with education, planning and referrals. The purpose of the program is to attempt to slow the expected rate of increase in DUI re-offenses once the interlock is removed. Success is measured by lower repeat DUI after the interlock is removed. There are two primary data sources to document an impact of supplemental services: the interlock's internal event recorder and the driving record. Over 2300 interlock offenders taking a median of more than 2000 breath tests, were studied during the full period the interlock was installed. The proportion of warn (i.e., BAC .02-.039% = 20-39 mg/dl) and fail (i.e., BAC 3.04% = 34O mg/dl) violations declined by over 50% during the course of the installed period. Those drivers in the intervention site were regularly interviewed about their drink-driving choices and encouraged to do more planning to separate drinking and driving. Findings show that those in the city where the intervention was offered were less likely to have fail-level BACs when attempting to start their cars. Other predictors of more failed BAC tests (as a proportion of all tests taken to start the car) include more reported drinking at baseline, being mandated to the interlock as a condition of license reinstatement, being unmarried, and having more prior offenses. The failed interlock BAC tests are a potent predictor of repeat DUI offenses after the interlock is removed. The 15% of the sample that fails BAC tests at the highest rate are two-three times more likely to have a repeat DUI during the first 12 months after the interlock is removed. For the covering abstract see ITRD E106992.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".