Development and evaluation of a scale assessing therapist fidelity to guidelines for delivering therapist-assisted Internet-delivered cognitive behaviour therapy
Bibliographic record
Abstract
Internet-delivered cognitive behaviour therapy (ICBT) is often accompanied by therapist emails, but there is limited research on the quality of this therapist-assistance. In this study, an ICBT Therapist Rating Scale (ICBT-TRS) was developed and evaluated to assess whether therapist emails showed fidelity to specific therapist behaviours. Using data from a previous ICBT trial for depression and anxiety, the ICBT-TRS was used to rate 706 emails sent by 39 therapists to 91 randomly selected patients. Emails were rated for adherence (absent/present) and quality (inadequate/competent) on the following behaviours: Builds Rapport, Seeks Feedback, Provides Symptom Feedback, Provides Psychoeducation, Facilitates Understanding, Praises Effort, Encourages Practice, Clarifies Administrative Procedures, and Communicates Effectively. Inter-rater reliability was high. Most behaviours were identified as present in 72-100% of emails, with the exception of Provides Symptom Feedback and Facilitating Understanding which were only present in 54 and 61% of emails. The majority of emails were rated as high quality (88-98% of messages). While not related to symptom improvement, ICBT-TRS ratings were higher when patients were more engaged in ICBT (e.g. log-ins) and among therapists who specialized in ICBT or had a background in Psychology. The ICBT-TRS has potential to facilitate ICBT research and clinical training.
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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 | 0.038 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".