Development of the Internet-Delivered Cognitive Behaviour Therapy Undesirable Therapist Behaviours Scale (ICBT-UTBS)
Bibliographic record
Abstract
Internet-delivered cognitive behaviour therapy (ICBT) is often provided with therapist assistance via asynchronous secure emails, but there is limited research on undesirable behaviours exhibited by therapists in their correspondence with patients. In this study, an ICBT-Undesirable Therapist Behaviour Scale (ICBT-UTBS) was developed and used to assess the nature, frequency, and correlates of undesirable therapist behaviours in routine practice. Thematic analysis was used to identify undesirable therapist behaviours in 720 emails sent to 91 randomly selected patients in the context of a previous clinical trial of transdiagnostic ICBT for depression and anxiety. The following undesirable behaviours were identified, albeit infrequently, in therapist emails: inadequate detail (6.4%), unaddressed content (4.0%), unsupportive tone (0.6%), missed correspondence (0.6%), inappropriate self-disclosure (0.6%), and unmanaged risk (0.3%). At least one undesirable behaviour was found in 10.7% of all emails coded. Moreover, 37.4% of patients received at least one email containing an undesirable therapist behaviour. Number of undesirable therapist behaviours was not correlated with patient engagement, working alliance, treatment satisfaction, or patient outcome variables. However, undesirable therapist behaviours were negatively correlated with patient gender and therapist characteristics (e.g., clinical setting, therapist profession). The results of the present study provide preliminary psychometric support for the ICBT-UTBS, a measure of ICBT treatment integrity. In the future, the ICBT-UTBS should be used in combination with the ICBT-Therapist Rating Scale (ICBT-TRS), a measure of desirable or recommended therapist behaviours, for training purposes and to monitor ICBT therapists in routine practice.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".