Using intervention mapping to evaluate ‘High-Alert,’ a brief smartphone intervention to reduce youth cannabis-impaired driving
Bibliographic record
Abstract
Youth driving under the influence of cannabis (DUIC) is a growing public health concern. While brief smartphone interventions have shown promise in reducing substance use and alcohol-impaired driving among youth, their efficacy for DUIC remains limited. Using the six-step Intervention Mapping framework, we developed and tested High Alert, a digital smartphone intervention designed to reduce DUIC among high-risk Canadian youth. The intervention was previously tested in a pilot randomized controlled trial comparing High Alert to an active control (exposure to six DUIC infographics) and a passive control (no contact). This study presents a comprehensive evaluation of High Alert using Step 6 of the Intervention Mapping framework. Reporting on this evaluation serves as a practical guide for researchers utilizing Intervention Mapping, offering valuable insights into High Alert's formative, process, outcome, and acceptability evaluations to enhance DUIC prevention efforts. Formative and acceptability evaluations revealed High Alert's positive reception among youth, with most participants willing to engage with it and recommend it to their peers. The program received high ratings for content and delivery, surpassing the static infographics used in the active control. Outcome evaluations demonstrated preliminary efficacy in reducing DUIC behaviour, particularly driving after cannabis co-use, compared to the no-contact group. Process evaluations highlighted implementation challenges, including online study bot activity, recruitment barriers (e.g., participant skepticism, limited ad targeting options), high attrition rates, and low adherence. Findings highlight the importance of Step 6 in Intervention Mapping, emphasizing the need for transparent and rigorous evaluation to inform future interventions. Addressing recruitment and implementation challenges is essential for improving the scalability and effectiveness of interventions targeting high-risk behaviours such as DUIC and will inform High Alert's future testing.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".