300 Unlocking keys to effecive ignitiion interlock programs to reduce alcohol impaired driving
Bibliographic record
Abstract
Background Ignition interlocks, when installed on vehicles of drivers convicted of alcohol-impaired driving (AID), reduce repeat arrest by 67%. However post-interlock removal, recidivism (AID re-arrest) among previous interlock users equals that of AID-convicted drivers who never used interlocks. Also, the low numbers of offenders installing interlocks limits the impact. Study objectives include determining interlock program characteristics associated with increased interlock use and evaluating including alcohol treatment in the program to reduce post-interlock recidivism. Methods To determine effective program characteristics, eight interlock program keys (e.g. requirement to instal interlocks) were identified and each rated on 1–5 scale for 28 U.S. state interlock programs. Correlation analysis between rate of interlocks in use/10,000 population, and program key rating was conducted. To evaluate treatment in the one state with a treatment program, survival analysis using Cox regression proportional hazards model was performed with post-interlock recidivism as the terminal event. The treatment group (n = 640) were offenders with three or more violations (two alcohol-positive start attempts within four hours) who completed alcohol treatment. A comparison group (n = 806) of those with one or two violations who did not attend alcohol treatment was created by matching to the treatment group on demographic and risk factors. Results The program keys most correlated with higher interlock rates were having a requirement to instal interlocks (r = 0.63), and monitoring to ensure interlocks are installed and used (r = 0.56). Incorporating alcohol treatment into an interlock program was effective with the treatment group experiencing 32% lower recidivism following interlock removal compared with the non-treatment group. Conclusions Strengthening program keys and incorporating treatment into interlock programs increases use of interlocks and reduces AID re-arrest.
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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 | 0.000 | 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) | 0.006 | 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".