Designing Practical Motivational Interviewing Training for Mental Health Practitioners Implementing Behavioral Lifestyle Interventions: Protocol for 3 Pilot Intervention Studies
Notice bibliographique
Résumé
BACKGROUND: Motivational interviewing (MI) is an evidence-based, patient-centered communication method shown to be effective in helping persons with serious mental illness (SMI) to improve health behaviors. In clinical trials where study staff conducted lifestyle interventions incorporating an MI approach, cardiovascular disease (CVD) risk profiles of participants with SMI showed improvement. Given the disproportionate burden of CVD in this population, practitioners who provide somatic and mental health care to persons with SMI are ideally positioned to deliver patient-centered CVD risk reduction interventions. However, the time for MI training (traditionally 16-24 hours), follow-up feedback, and the coaching required to develop and maintain patient-centered skills are significant barriers to incorporating MI when scaling up these evidence-based practices. OBJECTIVE: We describe the design and development of the following 2 scalable MI training approaches for community mental health practitioners: real-time brief workshops and follow-up asynchronous avatar training. These approaches are being used in 3 different pilot implementation research projects that address weight loss, smoking cessation, and CVD risk reduction in people with SMI who are a part of ALACRITY Center, a research-to-practice translation center funded by the National Institute of Mental Health. METHODS: Clinicians and staff in community mental health clinics across Maryland were trained to deliver 3 distinct evidence-based physical health lifestyle interventions using an MI approach to persons with SMI. The real-time brief MI workshop training for ACHIEVE-D weight loss coaches was 4 hours; IMPACT smoking cessation counselors received 2-hour workshops and prescribers received 1-hour workshops; and RHYTHM CVD risk reduction program staff received 4 hours of MI. All workshop trainings occurred over videoconference. The asynchronous avatar training includes 1 common didactic instructional module for the 3 projects and 1 conversation simulation unique to each study's target behavior. Avatar training is accessible on a commercial website. We plan to assess practitioners' attitudes and beliefs about MI and evaluate the impact of the 2 MI training approaches on their MI skills 3, 6, and 12 months after training using the MI Treatment Integrity 4.2.1 coding tool and the data generated by the avatar-automated scoring system. RESULTS: The ALACRITY Center was funded in August 2018. We have implemented the MI training for 126 practitioners who are currently delivering the 3 implementation projects. We expect the studies to be complete in May 2023. CONCLUSIONS: This study will contribute to knowledge about the effect of brief real-time training augmented with avatar skills practice on clinician MI skills. If MI Treatment Integrity scoring shows it to be effective, brief videoconference trainings supplemented with avatar skills practice could be used to train busy community mental health practitioners to use an MI approach when implementing physical health interventions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44830.
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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 | 0,039 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,011 |
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 ».