Assessing Competence in Collaborative Case Conceptualization: Development and Preliminary Psychometric Properties of the Collaborative Case Conceptualization Rating Scale (CCC-RS)
Bibliographic record
Abstract
BACKGROUND: Case conceptualization is assumed to be an important element in cognitive-behavioural therapy (CBT) because it describes and explains clients' presentations in ways that inform intervention. However, we do not have a good measure of competence in CBT case conceptualization that can be used to guide training and elucidate mechanisms. AIMS: The current study addresses this gap by describing the development and preliminary psychometric properties of the Collaborative Case Conceptualization - Rating Scale (CCC-RS; Padesky et al., 2011). The CCC-RS was developed in accordance with the model posited by Kuyken et al. (2009). METHOD: Data for this study (N = 40) were derived from a larger trial (Wiles et al., 2013) with adults suffering from resistant depression. Internal consistency and inter-rater reliability were calculated. Further, and as a partial test of the scale's validity, Pearson's correlation coefficients were obtained for scores on the CCC-RS and key scales from the Cognitive Therapy Scale - Revised (CTS-R; Blackburn et al., 2001). RESULTS: The CCC-RS showed excellent internal consistency (α = .94), split-half (.82) and inter-rater reliabilities (ICC =.84). Total scores on the CCC-RS were significantly correlated with scores on the CTS-R (r = .54, p < .01). Moreover, the Collaboration subscale of the CCC-RS was significantly correlated (r = .44) with its counterpart of the CTS-R in a theoretically predictable manner. CONCLUSIONS: These preliminary results indicate that the CCC-RS is a reliable measure with adequate face, content and convergent validity. Further research is needed to replicate and extend the current findings to other facets of validity.
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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 | 0.046 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".