Nonuse of Blended Web-Based and Face-To-Face Cognitive Behavioral Therapy for Alcohol Use Disorder: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: The use of digital technologies for health care has been the focus of social studies, which have concentrated on the digital divide between individuals who use technology and those who do not-with the latter often being considered as individuals with shortcomings. In Denmark, 91% of the population have computers and 97 out of 100 families have internet access, indicating that lack of access to technology is not the primary reason for nonuse. Although previous studies have primarily focused on participants' perspectives of using internet-based treatment for alcohol use disorder (AUD), no study has investigated individuals' reasons to prefer face-to-face treatment over blended face-to-face and internet-based cognitive behavioral therapy (bCBT) for AUD among treatment-seeking populations. OBJECTIVE: The aim of this qualitative study was to investigate the nonuse of bCBT among patients with AUD. Specifically, this study aims to explore patients' reasons for choosing not to receive treatment via this format. METHODS: This study was conducted among Danish patients with AUD who were enrolled in the study "Blending internet treatment into conventional face-to-face treatment for alcohol use disorder (Blend-A)" but had not used bCBT. The participant group consisted of 11 patients with AUD: 3 women and 8 men. The age range of the participants was 29-78 years (mean 59 years). Individual semistructured interviews were conducted using cell phones to gather participants' reasons for not choosing bCBT. The interviews were recorded, transcribed, and analyzed using thematic analysis. Five authors performed the analysis in 3 steps: (1) two authors read the transcripts and coded themes from their immediate impression of the material, (2) one author provided feedback, which was used to group overlapping themes together or create new themes that better reflected the content, and (3) the remaining two authors provided feedback on the analysis to improve its structure, readability, and relevance to the research aim. RESULTS: We found that the participants had various reasons for choosing face-to-face treatment over bCBT; these reasons were more related to personal matters and lesser to digital health literacy. We identified 4 themes related to personal matters for choosing face-to-face treatment over bCBT: (1) patients' need for attending sessions in person, (2) preference for verbal communication, (3) desire for immediate feedback, and (4) feeling more empowered and motivated with face-to-face sessions. CONCLUSIONS: This study provides valuable insight into participants' perspectives on blended therapy for AUD and highlights the importance of considering personal factors when designing digital health interventions. Our study indicates that most of the participants choose not to use bCBT for AUD because they perceive such treatment formats as impersonal. Instead, they prefer direct communication with the therapist, including the ability to express and comprehend facial expressions and body language. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12888-021-03122-4.
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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 | 0,009 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,005 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».