Dropout From an Internet-Delivered Cognitive Behavioral Therapy Intervention for Adults With Depression and Anxiety: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Treatment dropout continues to be reported from internet-delivered cognitive behavioral therapy (iCBT) interventions, and lower completion rates are generally associated with lower treatment effect sizes. However, evidence is emerging to suggest that completion of a predefined number of modules is not always necessary for clinical benefit or consideration of the needs of each individual patient. OBJECTIVE: The aim of this study is to perform a qualitative analysis of patients' experiences with an iCBT intervention in a routine care setting to achieve a deeper insight into the phenomenon of dropout. METHODS: A total of 15 purposively sampled participants (female: 8/15, 53%) from a larger parent randomized controlled trial were interviewed via telephone using a semistructured interview schedule that was developed based on the existing literature and research on dropout in iCBT. Data were analyzed using a descriptive-interpretive approach. RESULTS: The experience of treatment leading to dropout can be understood in terms of 10 domains: relationship to technology, motivation to start, background knowledge and attitudes toward iCBT, perceived change in motivation, usage of the program, changes due to the intervention, engagement with content, experience interacting with the supporter, experience of web-based communication, and termination of the supported period. CONCLUSIONS: Patients who drop out of treatment can be distinguished in terms of their change in motivation: those who felt ready to leave treatment early and those who had negative reasons for dropping out. These 2 groups of participants have different treatment experiences, revealing the potential attributes and nonattributes of dropout. The reported between-group differences should be examined further to consider those attributes that are strongly descriptive of the experience and regarded less important than those that have become loosely affiliated.
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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 | 0.018 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".