Cultural adaptation and validation of the Arabic version of the multidimensional cognitive attentional syndrome scale (MCASS)
Bibliographic record
Abstract
The cognitive attentional syndrome (CAS) is a core concept within metacognitive theory. The premise of the CAS is related to metacognition, however its role in psychopathology is distinct. Due to the complex nature of the CAS, a theoretically driven and psychometrically sound self-report measure of the CAS for the Arabic population is yet to be developed. We translated the Multidimensional Cognitive Attentional Syndrome Scale (MCASS) into the Arabic language and tested its structural validity. The MCASS was translated according to the standard guidelines of forward-translation followed by backward-translation. In Study 1, the MCASS was administered to a larger sample (N = 1027), selected from 22 Arabic-speaking countries in the Arab League countries, and exploratory factor analysis (EFA) was used to examine the factor structure of the measure. Those who participated in Study 1 were excluded from participating in Study 2. Confirmatory factor analysis (CFA) was used in Study 2 (N = 567) to assess the latent factor structure of MCASS, which supported a six-factor model. Results support multidimensional assessment of the CAS using the MCASS, and demonstrate suitability for use in Arab speaking samples. Implications of this study and recommendations for use of the Arabic version of MCASS are discussed.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".