Cross-cultural adaptation, validity, and reliability of the Persian Communicative Effectiveness Index for post-stroke aphasia: a preliminary study
Bibliographic record
Abstract
Background The Communicative Effectiveness Index (CETI) is a brief assessment scale designed to measure changes in the functional communication ability of people with post-stroke aphasia.Aims This study aimed to translate the CETI to the Persian language and measure its cross-cultural adaptation, validity, and reliability in persons with aphasia (PWA).Methods and Procedures The CETI was translated and adapted to Persian according to Beaton and colleagues’ (2000) guidelines. A cross-sectional study was then conducted to examine the psychometric properties of the Persian CETI (P-CETI), which was completed by 35 caregivers of PWA. Internal consistency and test-retest reliability of the P-CETI were evaluated using Cronbach’s alpha and the intra-class correlation coefficient (ICC), respectively. Concurrent validity was examined by correlating the P-CETI with the Functional Communication Performance Questionnaire in Adults with Brain Injury (P-FUCT).Outcomes and Results There were no culturally challenging items in the original CETI version identified during the adaptation process, with Persian-speaking participants finding the P-CETI understandable. The mean total P-CETI score was 51.8 ± 25.4 (range: 15.0–92.5). Cronbach’s alpha coefficient was 0.97, and all corrected item-total and inter-item correlations were in the acceptable range. The P-CETI also showed excellent test-retest reliability, with ICC = 0.99. The P-CETI significantly correlated with the P-FUCT, indicating acceptable concurrent validity.Conclusions The P-CETI can be considered a valid and reliable tool for the assessment of functional communication in Persian-speaking people with aphasia in clinical and research settings.
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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 | 0.013 |
| 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 | 0.001 |
| Open science | 0.000 | 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".