Developing a Smartphone-Based Adjunct Intervention to Reduce Cannabis Use Among Juvenile Justice-Involved Adolescents: Protocol for a Multiphase Study
Bibliographic record
Abstract
BACKGROUND: Adolescents involved in the juvenile justice system who use cannabis are at an increased risk of future substance use disorders and rearrest. Many court-involved, nonincarcerated (CINI) youth are referred for services in the community and often encounter multiple barriers to care, highlighting the need for minimally burdensome services that can be delivered in justice settings. Digital health interventions are accessible, easy to implement, and can provide ongoing support but have not been developed to address the unique needs of CINI youth who use cannabis. OBJECTIVE: This multiphase study will aim to develop, implement, and pilot test a novel smartphone app, Teen Empowerment through Computerized Health (TECH), to reduce cannabis and other substance use among CINI youth. TECH is conceptualized as a digital adjunct to a brief computerized intervention delivered by our family court partner. METHODS: Following the principles of user-centered design, phase I interviews with CINI youth aged 14-18 years (n=14-18), their caregivers (n=6-8), and behavioral health app developers (n=6-8) will guide the TECH design decisions. Next, in phase II, CINI youth (n=10) will beta test the TECH app prototype for 1 month; their feedback regarding feasibility and acceptability will directly inform the app refinement process. Finally, in phase III, CINI youth (n=60) will participate in a pilot randomized controlled trial for 6 months, comparing the preliminary effectiveness of the adjunctive TECH app on cannabis use outcomes. RESULTS: Phase I data collection began in September 2020 and was completed in December 2021; 14 CINI youth, 8 caregivers, and 11 behavioral health app developers participated in the study. Phases II and III will occur in 2022 and 2023 and 2023 and 2025, respectively. CONCLUSIONS: This body of work will provide insight into the feasibility and acceptability of a smartphone-based adjunctive intervention designed for CINI youth. Phase III results will offer a preliminary indication of the effectiveness of the TECH app in reducing cannabis use among CINI youth. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35402.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.057 | 0.011 |
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".