Psychometric properties of Persian version of Cognitive Behavioural Avoidance Scale: results from student, general population and clinical samples in Iran
Bibliographic record
Abstract
BACKGROUND: There is no published evidence about the psychometric properties of the Cognitive Behavioral Avoidance Scale (CBAS) in Eastern cultures. AIMS: The current research evaluated the psychometric properties of a Persian version of the CBAS. METHOD: The research consisted of two studies. In Study 1, a university student sample (n = 702) completed the CBAS, the Beck Depression Inventory-II, the Thought Control Questionnaire and the Anxious Thoughts Inventory. In Study 2, a general population sample (n = 384) and a clinical sample (n = 152) completed the CBAS, the Young Compensation Inventory and the Depression, Anxiety, Stress Scale-21. RESULTS: Exploratory factor analysis of the data from Study 1 suggested a four-factor solution for CBAS. The CBAS had acceptable internal consistency and test-re-test reliability, and showed significant correlations with depression symptoms and anxious thoughts. Confirmatory factor analysis of the data from Study 2 indicated good fit between the four-factor model and data. The CBAS had a significant relationship with depression, anxiety and stress symptoms, but no associations with schema compensatory behaviour strategy. Finally, the CBAS and its subscales successfully distinguished a clinical sample from a general population sample. CONCLUSIONS: The findings provide preliminary evidence for reliability and validity of the CBAS among Iranian student, general population and clinical samples.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".