Developing a mobile-based brief intervention to reduce cannabis-impaired driving among youth: An intervention mapping approach
Bibliographic record
Abstract
Behaviour change interventions delivered via smartphones have the potential to reduce youth cannabis use and driving under the influence of cannabis (DUIC). Countless smartphone applications (either downloadable or web-based) are available to help reduce substance use and impaired driving. However, most are developed without evidence-based content and theory, and many have poor user engagement. This study aims to: (1) describe the systematic development and theoretical foundations of a youth DUIC smartphone intervention, and (2) describe the pre-testing among a sample of youth and adult cannabis educators (prior to efficacy testing). A 6-step Intervention Mapping approach was utilized to combine theory, evidence, and user feedback to develop and implement the 'High Alert' intervention. This evidence-based and iterative process entailed: (1) conducting a needs assessment, (2) identifying intervention objectives, which map on the following DUIC determinants: knowledge, attitudes, risk perception, perceived norms, and self-efficacy, (3) selecting intervention theory and design, (4) developing of the intervention, (5) implementation, and (6) evaluation. Application of Intervention Mapping resulted in a smartphone web-based application that could support reductions in cannabis use and DUIC. The 'High Alert' intervention was created to include four modules with contents focusing on educating youth on the dangers and legal risks of DUIC, limiting risky situations, avoiding riding with an impaired driver, planning a safe ride home, and promoting safer cannabis use. Future research will test the efficacy of the intervention in reducing risky cannabis use and DUIC among youth.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".