What are Clients Asking Their Therapist During Therapist-Assisted Internet-Delivered Cognitive Behaviour Therapy? A Content Analysis of Client Questions
Bibliographic record
Abstract
BACKGROUND: Although internet-delivered cognitive behaviour therapy (ICBT) yields large clinical outcomes when accompanied by therapeutic support, a portion of clients do not benefit from treatment. In ICBT, clients review treatment materials online typically on a weekly basis. A key component of therapist-assistance involves answering questions as clients review and work on assignments related to the treatment materials. AIMS: The goal of this study was to enhance understanding of the nature of client questions posed during ICBT and examine potential associations between the number of questions asked and treatment outcomes in order to provide insight into how to improve ICBT for future users. METHOD: Content analysis was used to qualitatively analyse and identify questions that 80 clients asked their designated therapist over the course of an 8-week ICBT programme for anxiety and depression. RESULTS: On average, clients sent six emails during the course of treatment, of which less than two questions were asked. Of the 137 questions posed by clients, 46.72% reflected questions designed to enhance understanding and apply the material and techniques reviewed in the programme. Additional questions were categorized as clarifying the therapeutic process (22.62%), addressing technical challenges (18.25%), and seeking assistance with problems outside the scope of ICBT (12.41%). Number of client questions asked was not significantly correlated with the number of lessons completed, symptom change, or perceptions of therapeutic alliance. CONCLUSIONS: Findings can inform future practitioners who deliver ICBT of what to expect with this treatment approach and also assist in the development of future ICBT programmes.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".