Nonuse of Blended Web-Based and Face-To-Face Cognitive Behavioral Therapy for Alcohol Use Disorder: Qualitative Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".