Metric qualities of the cognitive behavioral assessment for outcome evaluation to estimate psychological treatment effects
Bibliographic record
Abstract
BACKGROUND: Cognitive behavioral assessment for outcome evaluation was developed to evaluate psychological treatment interventions, especially for counseling and psychotherapy. It is made up of 80 items and five scales: anxiety, well-being, perception of positive change, depression, and psychological distress. The aim of the study was to present the metric qualities and to show validity and reliability of the five constructs of the questionnaire both in nonclinical and clinical subjects. METHODS: Four steps were completed to assess reliability and factor structure: criterion-related and concurrent validity, responsiveness, and convergent-divergent validity. A nonclinical group of 269 subjects was enrolled, as was a clinical group comprising 168 adults undergoing psychotherapy and psychological counseling provided by the Italian public health service. RESULTS: Cronbach's alphas were between 0.80 and 0.91 for the clinical sample and between 0.74 and 0.91 in the nonclinical one. We observed an excellent structural validity for the five interrelated dimensions. The clinical group showed higher scores in the anxiety, depression, and psychological distress scales, as well as lower scores in well-being and perception of positive change scales than those observed in the nonclinical group. Responsiveness was large for the anxiety, well-being, and depression scales; the psychological distress and perception of positive change scales showed a moderate effect. CONCLUSION: The questionnaire showed excellent psychometric properties, thus demonstrating that the questionnaire is a good evaluative instrument, with which to assess pre- and post-treatment outcomes.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".