Randomized Trial Shows Smartphone Support App for DWI Offenders and Their Families Reduced Alcohol Use and Ignition Interlock Device Lockouts
Bibliographic record
Abstract
Objective: Driving while intoxicated (DWI) remains a preventable source of morbidity and mortality in the United States. Ignition Interlock Devices (IID) are used to prevent DWI offenders from driving while intoxicated during a mandated installation period and are effective during that time. Once IIDs are removed, DWI rates are similar to levels of offenders who had no IID. This study tested the efficacy of a smartphone app (B-SMART) for DWI offenders with an IID and Concerned Family Members (CFMs), with the goal of reducing IID lockout events and alcohol consumption. Method: Four B-SMART app modules were developed: 1) Life with Interlock, 2) Supporting Changes in Drinking, 3) Doing Things Together, and 4) Effective Communication. Participants (pairs of DWI offenders and CFMs) were randomly assigned to receive the B-SMART app (n=58) or referral to a state IID information page (Usual and Customary - UC condition, n=65) and followed for 9 months post-randomization. IID data (failed tests and lockout events) were obtained from IID providers as the primary outcome variables. Offender and CFM reports of alcohol consumption in the last 30 days prior to assessment were secondary measures. Results: IID data were collected on 62% (N=76) of participants. B-SMART participants had significantly fewer lockout events than UC participants. B-SMART offenders and their CFMs reported significantly less likelihood of DWI offender drinking at 9-months. Conclusions: Results suggest the B-SMART app reduced IID lockout events and DWI offender alcohol consumption. These outcomes are important because fewer IID lockout events predict lower DWI recidivism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".