Supplemental Intervention for Alcohol Use Disorder Treatment Patients With a Co-Occurring Anxiety Disorder: Technical Development and Functional Testing of an Autonomous Digital Program
Notice bibliographique
Résumé
Background: Anxiety disorders are common in alcohol use disorder (AUD) treatment patients. Such co-occurring conditions ("comorbidity") have negative prognostic implications for AUD treatment outcomes, yet they commonly go unaddressed in standard AUD care. Over a decade ago, we developed and validated a cognitive behavioral therapy intervention to supplement standard AUD care that, when delivered by trained therapists, improves outcomes in comorbid patients. However, this validated intervention, like many others in addiction care, has not been taken up in community-based AUD treatment programs. This phenomenon-empirically validated treatments that fail to be widely adopted in community care-has been termed the "research-to-practice gap." Researchers have suggested that the availability of fully autonomous digital equivalents of validated therapist-delivered therapies could reduce some barriers underlying the research-to-practice gap, especially by eliminating the need for costly and intensive therapist training and supervision. Objective: With this in mind, we obtained a Program Development Grant (R34) to conduct formative work in the development of a fully autonomous digital version of our previously validated therapist-delivered intervention for AUD treatment patients with a comorbid anxiety disorder. Methods: In the first phase of the project, we developed the digital intervention. This process included: (1) identifying appropriate collaborators and vendors; (2) consultation with an e-learning expert to develop a storyboard and accompanying graphics and narrative; (3) video production and editing; and (4) interactive programming. The second phase of the project was functional testing of the newly developed digital intervention conducted in 52 residential AUD treatment patients with a comorbid anxiety disorder. Patients underwent the 3 one-hour segments of the newly developed intervention and completed user surveys, knowledge quizzes, and behavioral competence tests. Results: While the development of the digital intervention was successful, the timeline was approximately double that projected (1 vs 2 years) due to false starts and inefficiencies that we describe, including lessons learned. Functional testing of the newly developed digital intervention showed that, on average, patients rated the user experience in the upper (favorable) 20% of the response scales. Knowledge quizzes and behavioral demonstrations showed that over 80% of participants gained functional mastery of the key skills and information taught in the program. Conclusions: Functional testing results in this study justify a randomized controlled trial of the digital intervention's efficacy, which is currently ongoing. In sharing the details of our challenges and solutions in developing the digital intervention, we hope to inform others developing digital tools. The extent to which the availability of empirically validated, fully autonomous digital interventions achieves their potential to reduce the research-to-practice gap remains an open but important empirical question. The present work stands as a necessary first step toward that end.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».