Perceptions of client stories in internet-delivered cognitive behaviour therapy: A mixed-methods evaluation
Bibliographic record
Abstract
Internet-delivered Cognitive-Behavioural Therapy (ICBT) aims to support people with mental health concerns using online treatment materials. Client stories (either real or a composite based on many clients) are often used in ICBT to facilitate learning. However, these stories remain understudied in terms of how they are perceived by clients, as well as their relationship to ICBT engagement, satisfaction, and outcomes. Among a sample of 324 clients enrolled in transdiagnostic ICBT targeting symptoms of depression and anxiety, we examined client perceptions of stories through mixed-method qualitative (open-ended) and quantitative (closed-ended) data collection. Specifically, 234 (72.22 %) clients responded to questions about stories at 4 weeks and 221 (68.21 %) responded to questions at 8 weeks. Most clients who responded to questions endorsed reviewing at least some stories (79.06 % at 4 weeks, 71.95 % at 8 weeks). Moreover, they rated stories positively in terms of being relatable, making clients feel less alone, increasing knowledge, providing ideas for how to use skills, and motivating clients to use skills. These perceptions of stories remained stable over the course of treatment. Stories were perceived more positively among those with lower symptom severity at 8 weeks as well as those who were more satisfied with ICBT at 8 weeks. Story perceptions at 4 weeks were predictive of decreased post-treatment anxiety symptom severity but not depression while controlling for baseline scores, age, and education. 26.49 % of clients at 4 weeks who reviewed stories and 33.33 % at 8 weeks provided suggestions about how to improve stories. In a qualitative analysis, we found 5 categories of suggestions including increasing the variety of issues and relatability of stories, ensuring the stories are realistic, refining the formatting, and making the stories shorter. Overall, this study provides insights into how client stories could be improved to play a more significant role in future ICBT programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".