Feasibility Testing a Meditation App for Professionals Working With Youth in the Legal System: Protocol for a Hybrid Type 2 Effectiveness-Implementation Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Probation officers and other professionals who work with youth in the legal system often experience high chronic workplace stress, which can contribute over time to elevations in anxiety, depression, and workplace burnout. Emotion dysregulation appears to function as a common mechanism underlying these elevations, and growing evidence suggests it can be improved with mindfulness meditation. Implemented successfully, app-based meditation programs could provide professionals with real-time tools for mitigating the effects of chronic workplace stress. OBJECTIVE: This paper describes the protocol for a hybrid type 2 effectiveness-implementation pilot randomized controlled trial (RCT) of Bodhi AIM+, a meditation app adapted with and for professionals who work with youth in the legal system. The adaptation process and implementation plan, as well as the pilot RCT design, were guided by theoretically driven implementation science frameworks. The primary outcome of the pilot RCT is app adherence (ie, ongoing app usage per objective analytics data). METHODS: The RCT will be fully remote. Officers and other professionals who work with youth in the legal system (N=50) will be individually randomized to use the meditation app or an active control app matched for time and structure. All participants will be asked to follow a 30-day path of brief audio- or video-guided content and invited to use additional app features as desired. In-app analytics will capture the objective usage of each feature. An adaptive engagement design will be employed to engage nonusers of both apps, whereby analytics data indicating nonuse will trigger additional support (eg, text messages promoting engagement). Mental health outcomes and potential moderators and covariates will be self-reported at baseline, posttest, and 6 months. Participants will also complete 1-week bursts of ecological momentary assessment (EMA) at baseline and over the last week of the intervention to capture the mechanistic target (ie, emotion regulation) in real time. All participants will be invited to complete qualitative posttest interviews. Descriptive statistics will be calculated for quantitative data. Qualitative data will be analyzed using a combined deductive-inductive approach. The quantitative and qualitative data will be incorporated into a mixed methods triangulation design, allowing for the evaluation of app adherence and other implementation outcomes as well as related barriers and facilitators to implementation. RESULTS: Enrollment into the trial started in December 2024 and is currently underway. Study results are anticipated to be available in 2026. CONCLUSIONS: Completion of this pilot trial will inform a future, fully powered RCT to formally evaluate the effectiveness and implementation of Bodhi AIM+. Its use of implementation science methods, coupled with digital technology, positions the present study not only to help make meditation tools available to an important workforce at scale but also to inform broader efforts at implementing and evaluating health apps within workplace settings. TRIAL REGISTRATION: ClincialTrials.gov NCT06555172; https://clinicaltrials.gov/study/NCT06555172. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71867.
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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 | 0.049 | 0.049 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".