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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,004 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».