Therapist behaviours in internet-based cognitive behaviour therapy (ICBT) for depressive symptoms
Bibliographic record
Abstract
Internet-based cognitive behaviour therapy (ICBT) is efficacious for treating depression, with therapist guidance identified as important for favourable outcomes. We have limited knowledge, however, about the fundamental components of therapist guidance in ICBT. The purpose of this study was to systematically examine therapist messages sent to patients during the course of ICBT for depressive symptoms in order to identify common “therapist behaviours” and the extent to which these behaviours correlate with completion of modules and improvements in symptoms at post-treatment, one- and two-year follow-up. A total of 664 e-mails from 5 therapists to 42 patients were analysed using qualitative content analysis. The most frequent behaviour was encouraging that accounted for 31.5% of the total number of coded behaviours. This was followed by affirming (25.1%), guiding (22.2%) and urging (9.8%). Less frequently the therapists clarified the internet treatment framework , informed about module content , emphasised the importance of patient responsibility , confronted the patient and made self-disclosures . Six of the nine identified therapist behaviours correlated with module completion. Three behaviours correlated with symptom improvement. Affirming correlated significantly ( r = .42, p = .005) with improvement in depressive symptoms at post-treatment and after two years ( r = .39, p = .014). Encouraging was associated with outcome directly after treatment ( r = .52, p = .001). Self-disclosure was correlated with improvement in depressive symptoms at post-treatment ( r = .44, p = .003). The study contributes to a better understanding of therapist behaviours in ICBT for depressive symptoms. Future directions for research 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".