Exploring Whether Addictions Counselors Recommend That Their Patients Use Websites, Smartphone Apps, or Other Digital Health Tools to Help Them in Their Recovery: Web-Based Survey
Notice bibliographique
Résumé
BACKGROUND: Hundreds of smartphone apps or websites claiming to help those with addictions are available, but few have been tested for efficacy in changing clinically relevant addictions outcomes. Although most of these products are designed for self-facilitation by users struggling with addictions, counselors and other addictions treatment providers will likely play a critical role in facilitating adoption by integrating their use into counseling or recommending them to their patients. Yet, few studies have explored the practices of addictions counselors in using or recommending addictions-focused digital health tools in their work. OBJECTIVE: The aim of this study was to understand whether addiction counselors are recommending that their patients use addictions-focused apps to help them in their recovery, and the factors that affect their desire to do so. METHODS: Licensed addiction counselors practicing in the United States (N=112) were recruited from professional and scientific organizations of alcohol or drug counselors to complete a web-based survey. RESULTS: In total, 74% (83/112) of counselors had recommended that their patients use a website or smartphone app to assist them in recovery, and those that had done so reported recommending an app with an average of 54% of their patients. The most commonly recommended app or website was SMARTRecovery.org (9%), I am Sober (8%), In the Rooms (7%), Insight Timer (4%), Calm (4%), Sober Tool (4%), Recovery Box (3%), and Sober Grid (3%). The most important reason that counselors recommended the websites or apps was that colleagues or patients told them they found it helpful (55%), followed by their workplaces recommending it (20%) and professional organizations recommending it (10%). Counselors' intentions to recommend a hypothetical app were strongest for apps that had been tested in rigorous, scientific studies that showed they helped users stay sober or reduce their substance use; 94% (105/112) reported that they would "definitely" or "probably" use such an app. CONCLUSIONS: Most addictions counselors surveyed are already recommending that their patients use apps or websites to help them in their recovery, despite the paucity of available products that have evidence supporting their efficacy for addictions outcomes. One way that product developers could increase adoption among addictions treatment providers is to make efficacy testing a priority and to disseminate results through professional organizations and clinics.
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».