Patients’ Experiences of Web- and Mobile-Assisted Group Therapy for Depression and Implications of the Group Setting: Qualitative Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: Blended group therapy combines group sessions with Web- and mobile-based treatment modules. Consequently, blended group therapy widens the choice within blended interventions at reasonable costs. This is the first qualitative study on blended group therapy. OBJECTIVE: The objective of this study was to investigate the patient-centered feasibility of blended group therapy for major depression, with special emphasis on the fit and dynamic interplay between face-to-face and internet-based elements. METHODS: A total of 22 patients who had a variety of experiences through participating in one of the two blended group therapy interventions were interviewed following a semistructured interview guide. In-depth interviews were analyzed by three trained psychologists, using thematic analysis and a rule-guided internet-based program (QCAmap). The transcript of the interviews (113,555 words) was reduced to 1081 coded units, with subsequent extraction of 16 themes. RESULTS: Web- and mobile-based elements were described as a treatment facilitator and motivator, increasing the salience and consolidation of cognitive behavioral therapy materials, resulting in in- and inter-session alignment to the treatment. Additionally, patients valued the option of intimate Web-based self-disclosure (by lateral patient-therapist communication), and therapists were provided with tools for between-session monitoring and reinforcement of exercising. In this context, group phenomena seemed to back up therapists' efforts to increase treatment engagement. The dissonance because of noncompliance with Web-based tasks and the constriction of in-session group interaction were considered as possible negative effects. Finally, issues of tailoring and structure seemed to fulfill different preconditions compared with individual therapy. CONCLUSIONS: Blended group therapy constitutes a structured and proactive approach to work with depression, and the integration of both modalities initiates a beneficial interplay. Results support the patient-centered value of blended group therapy and provide the first insight into blended group therapy's role in fostering therapeutic treatment factors. However, potential negative effects should be considered carefully.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".