Translation, Cross-Cultural Adaptation, and Psychometric Validation of the Chinese/Mandarin Cardiac Rehabilitation Barriers Scale (CRBS-C/M)
Bibliographic record
Abstract
Objective. Cardiovascular diseases are among the leading causes of morbidity in China and around the world. Cardiac rehabilitation (CR) effectively mitigates this burden; however, utilization is low. CR barriers in China have not been well characterized; this study sought to translate, cross-culturally adapt, and psychometrically validate the CR Barriers Scale in Chinese/Mandarin (CRBS-C/M). Methods. Independent translations of the 21-item CRBS were conducted by two bilingual health professionals, followed by back-translation. A Delphi process was undertaken with five experts to consider the semantics and cross-cultural relevance of the items. Following finalization, 380 cardiac patients from 11 hospitals in Shanghai were administered a validation survey including the translated CRBS. Following exploratory and confirmatory factor analysis, internal consistency was assessed. Validity was tested through assessing the association of the CRBS-C/M with the CR Information Awareness Questionnaire. Results. Items were refined and finalized. Factor analysis of CRBS-C/M ( Kaiser Meyer Olkin = 0.867 , Bartlett’s test p < 0.001 ) revealed five factors: perceived CR need, external logistical factors, time conflicts, program and health system-level factors, and comorbidities/lack of vitality; Cronbach’s alpha ( α ) of the subscales ranged from 0.67 to 0.82. The mean total CRBS score was significantly lower in patients who participated in CR compared with those who did not, demonstrating criterion validity ( 2.35 ± 0.71 vs. 3.08 ± 0.55 ; p < 0.001 ). Construct validity was supported by the significant associations between total CRBS scores and CR awareness, sex, living situation, city size, income, diagnosis/procedure, disease severity, and several risk factors (all p < 0.05 ). Conclusions. CRBS-C/M is reliable and valid, so barriers can be identified and mitigated in Mandarin-speaking patients.
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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 | 0.010 | 0.014 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".