Assessing Therapist Competence: Development of a Performance-Based Measure and Its Comparison With a Web-Based Measure
Bibliographic record
Abstract
BACKGROUND: Recent research interest in how best to train therapists to deliver psychological treatments has highlighted the need for rigorous, but scalable, means of measuring therapist competence. There are at least two components involved in assessing therapist competence: the assessment of their knowledge of the treatment concerned, including how and when to use its strategies and procedures, and an evaluation of their ability to apply such knowledge skillfully in practice. While the assessment of therapists' knowledge has the potential to be completed efficiently on the Web, the assessment of skill has generally involved a labor-intensive process carried out by clinicians, and as such, may not be suitable for assessing training outcome in certain circumstances. OBJECTIVES: The aims of this study were to develop and evaluate a role-play-based measure of skill suitable for assessing training outcome and to compare its performance with a highly scalable Web-based measure of applied knowledge. METHODS: Using enhanced cognitive behavioral therapy (CBT-E) for eating disorders as an exemplar, clinical scenarios for role-play assessment were developed and piloted together with a rating scheme for assessing trainee therapists' performance. These scenarios were evaluated by examining the performance of 93 therapists from different professional backgrounds and at different levels of training in implementing CBT-E. These therapists also completed a previously developed Web-based measure of applied knowledge, and the ability of the Web-based measure to efficiently predict competence on the role-play measure was investigated. RESULTS: The role-play measure assessed performance at implementing a range of CBT-E procedures. The majority of the therapists rated their performance as moderately or closely resembling their usual clinical performance. Trained raters were able to achieve good-to-excellent reliability for averaged competence, with intraclass correlation coefficients ranging from .653 to 909. The measure was also sensitive to change, with scores being significantly higher after training than before as might be expected (mean difference 0.758, P<.001) even when taking account of repeated data (mean difference 0.667, P<.001). The major shortcoming of the role-play measure was that it required considerable time and resources. This shortcoming is inherent in the method. Given this, of most interest for assessing training outcome, scores on the Web-based measure efficiently predicted therapist competence, as judged by the role-play measure (with the Web-based measure having a positive predictive value of 77% and specificity of 78%). CONCLUSIONS: The results of this study suggest that while it was feasible and acceptable to assess performance using the newly developed role-play measure, the highly scalable Web-based measure could be used in certain circumstances as a substitute for the more labor-intensive, and hence, more costly role-play method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".