Preliminary efficacy of ‘high-alert’ a brief smartphone intervention to reduce Cannabis-impaired driving among youth: A pilot randomized controlled trial
Bibliographic record
Abstract
Introduction Driving under the influence of cannabis (DUIC) is a growing public health concern, particularly among young drivers. This pilot study explores the short-term preliminary efficacy of High Alert, a brief smartphone intervention designed to reduce DUIC among youth. Methods An online pilot randomized controlled trial was conducted with 102 youth aged 18–24 who had a history of DUIC (≥3 times in the past 3 months). Participants were randomized into three groups: High Alert (n = 37), Active Control (n = 34), or Passive Control (n = 31). High Alert included two web-based sessions on cannabis and DUIC education. The Active Control received a single session reviewing six DUIC-related infographics, while the Passive Control received no intervention. The primary outcome was self-reported DUIC incidents (alone or with other substances) over 3 months, assessed at baseline and 3-month follow-up. Results Among the 52 participants who completed the 3-month follow-up (High Alert: n = 16; Active Control: n = 16; Passive Control: n = 20), High Alert showed the greatest mean reduction in DUIC incidents (-7.44, Cohen’s d = -0.40), compared to Active Control (-3.62, d = -0.49) and Passive Control (-3.05, d = -0.38). The reduction was statistically significant compared to Passive Control (β = -0.61, p = .03), but not Active Control (β = -0.08, p = .781). Conclusions Preliminary findings suggest that High Alert may show promise in reducing self-reported DUIC behaviours compared to a no-contact control group, but additional research with larger samples and longer follow-ups is needed. ClinicalTrials.gov registration NCT06098573 .
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".