Assessing clinician competence in the delivery of cognitive-behavioural therapy for eating disorders: development of the Cognitive-Behavioural Therapy Scale for Eating Disorders (CBTS-ED)
Bibliographic record
Abstract
Evidence-based cognitive-behaviour therapy for eating disorders (CBT-ED) differs from other forms of CBT for psychological disorders, making existing generic CBT measures of therapist competence inadequate for evaluating CBT-ED. This study developed and piloted the reliability of a novel measure of therapist competence in this domain-the Cognitive Behaviour Therapy Scale for Eating Disorders (CBTS-ED). Initially, a team of CBT-ED experts developed a 26-item measure, with general (i.e. present in every session) and specific (context- or case-dependent) items. To determine statistical properties of the measure, nine CBT-ED experts and eight non-experts independently observed six role-played mock CBT-ED therapy sessions, rating the therapists' performance using the CBTS-ED. The inter-item consistency (Cronbach's alpha and McDonald's omega) and inter-rater reliability (ICC) were assessed, as appropriate to the clustering of the items. The CBTS-ED demonstrated good internal consistency and moderate/good inter-rater reliability for the general items, at least comparable to existing generic CBT scales in other domains. An updated version is proposed, where five of the 16 "specific" items are reallocated to the general group. These preliminary results suggest that the CBTS-ED can be used effectively across both expert and non-expert raters, though less experienced raters might benefit from additional training in its use.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".