Content of client emails in internet-delivered cognitive behaviour therapy: A comparison between two trials and relationship to client outcome
Bibliographic record
Abstract
Many Internet-delivered cognitive behavioural therapy (ICBT) programs include email communication between clients and therapists as a part of treatment; yet relatively little is known about the nature and impact of this communication. Previous research conducted by Svartvatten et al. (2015) has identified 10 themes in written correspondence by clients accessing ICBT for depression. The current study examined: (1) if previously identified themes in client emails would be present in a shorter ICBT program for depression and anxiety; and (2) whether themes in emails similarly correlated with symptom improvement, lesson completion, and perceptions of working alliance. Using 80 randomly selected clients from a published ICBT trial (ISRCTN42729166; Hadjistavropoulos et al., 2016), client emails (average 5.69 per client) were examined for the presence of the themes reported by Svartvatten et al. (2015) and correlated with symptom improvement, lesson completion, perceptions of working alliance. Although most themes developed by Svartvatten et al. (2015) were identified in client emails, the frequency of themes differed between studies. Most notably, emails in the current study were more often coded as involving alliance bolstering (~39% vs. 22% of statements) and identification of patterns and problem behaviours (~25% vs. 6% of statements). Greater frequency of tries alternative behaviour and identifies patterns and problem behaviours were correlated with a greater number of lessons completed. In terms of symptom change, greater frequency of maladaptive repetitive thinking and problems with treatment content in the emails were correlated with smaller improvements in anxiety, whereas observes positive consequences was correlated with larger improvements in anxiety. Similarly, greater frequency of maladaptive repetitive thinking was correlated with smaller improvements in depression. Regarding perceptions of working alliance, more frequent statements of observes positive consequences was correlated with higher alliance. The research provides clinicians and researchers with an improved understanding of the comparability and meaning of client communication in different ICBT programs. Experimental research is needed to better understand the role of client communication in 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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".