Who benefits most from therapist-assisted internet-delivered cognitive behaviour therapy in clinical practice? Predictors of symptom change and dropout
Bibliographic record
Abstract
Internet-delivered cognitive behavioral therapy (ICBT) is effective for treating anxiety and depression, but not for all patients. Predictors of dropout and outcomes from ICBT remain unclear and the literature could benefit from study of response to ICBT among larger community samples using advanced statistical techniques. In this study, we sought to identify predictors of dropout and symptom change in a large community sample (n = 1201) who received therapist-assisted transdiagnostic ICBT targeting anxiety and/or depression. Logistic regression was used to assess dropout, and showed that those who fully completed ICBT lessons (n = 880) were older and endorsed lower psychological distress at intake than those who only partially completed ICBT lessons (n = 321). During the course of therapy, patients responded to the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 at six time points. Autoregressive latent trajectory models were fitted to this data to assess the ability of demographic variables, program engagement, psychological and medical service usage, and psychological distress to explain individual variance in initial symptom levels and symptom change over time. Higher symptom scores at pre-treatment were predictive of greater symptom improvement. Symptom improvement was greater in those who were off work on disability and those without higher post-secondary education. Clinical implications are discussed.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".