Understanding Client Difficulties in Transdiagnostic Internet-Delivered Cognitive Behaviour Therapy: A Qualitative Analysis of Homework Reflections
Bibliographic record
Abstract
Internet-delivered cognitive behaviour therapy (ICBT) is helpful for many clients, but less is known about the challenges clients face during ICBT, such as difficulties with skill practice, development, or maintenance. Understanding client difficulties can help therapists support clients with skill development and prevent treatment drop-out, but has not been systematically studied. This study included a conventional content analysis of clients' responses to a homework reflection question about difficulties with lessons and skills. Data was drawn from a previously published trial of 301 clients who were randomly assigned to receive homework reflection questions during ICBT. A decreasing number of clients responded to the question about skill difficulties with each lesson. Clients who answered the question about difficulties were more engaged with ICBT (i.e., more lessons completed, logins, days enrolled in ICBT, and messages sent to therapists). Clients shared skill-specific challenges (including initial challenges and more advanced challenges), generic challenges (content or skills being cognitively draining or emotionally draining, contextual challenges, forgetfulness, limited time, and lack of familiarity with the skill), or no challenges. Thought challenging (59.6%) and graded exposure (57.5%) were associated with the greatest number of skill-specific challenges. Findings can help therapists anticipate and address common client challenges during ICBT.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 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".