Cross-cultural adaptation and psychometric validation of the Persian version of the Cardiac Rehabilitation Barriers Scale (CRBS-P)
Bibliographic record
Abstract
Objectives This study aimed to translate, cross-culturally adapt and psychometrically validate a Persian version of the Cardiac Rehabilitation Barriers Scale (CRBS-P) and to identify the main barriers in an Iranian setting. Setting Afshar cardiac rehabilitation (CR) centre, affiliated with the Yazd University of Medical Sciences, in the centre of Iran. Design This was a multimethod study, culminating in a cross-sectional survey. Participants Inpatient CR graduates who did not attend their initial outpatient CR appointment. Method The 21-item CRBS was translated and cross-culturally adapted in accordance with best practices; an expert panel considered the items and previous non-attending patients were interviewed via phone to refine the scale. Next, structural validity was assessed; participants were invited to complete the CRBS on the phone between March 2017 and February 2018. Using exploratory factor analysis (EFA) with principal component analysis extraction and oblique rotation. Second, confirmatory factor analysis (CFA) was used to verify the results; several goodness-of-fit indices were considered. The internal consistency and 3-week test–retest reliability of the scale (5% subsample) were evaluated using Cronbach’s α and intraclass correlation (ICC), respectively. Results Face, content and cross-cultural validity were established by the experts and patients (n=50). One thousand and one hundred (40.7%) of the 2700 patients completed the CRBS-P. Structural validity was established by EFA (Bartlett’s test p<0.001; =0.759) and confirmed by the CFA; a four-factor solution with 18 items accounting for 61.256% of variance had the best fit (χ 2 /df=3.206, root mean square error of approximation=0.061 and Comparative Fit Index=0.959). The internal consistency and test–retest reliability (n=42) of the scale were acceptable (ICC=0.743 95% CI (0.502 to 0.868); overall α=0.797). The top barriers were not knowing about CR, cost and lack of encouragement from physicians. Conclusion The four-factor, 18-item CRBS-P had good psychometric properties, and hence can be reliably and validly used to measure CR barriers in Iran and other Persian-speaking populations.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 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".