Translation, cultural adaptation, and psychometric validation of the Provider Attitudes toward Cardiac Rehabilitation and Referral (PACRR-C) Scale in Simplified Chinese
Bibliographic record
Abstract
BACKGROUND: The Provider Attitudes toward CR and Referral (PACRR) scale was translated into Simplified Chinese and psychometric validation ensued. METHODS: Brislin's Translation Model was applied, with two independent forward translations followed by back-translation. Experts assessed the face, content and cross-cultural validity of items, and item analysis followed. For validation, 227 physicians from hospitals in 14 Chinese provinces completed the PACRR-C. Structural validity was assessed through exploratory and confirmatory factor analysis. Internal and split-half reliability were assessed. RESULTS: Some items were rephrased and one item was deleted. The content validity index for the total scale was 0.965. The correlation coefficients between the 18 items and the total scale ranged between 0.28 and 0.76. Consistent with the English version, four factors were extracted (Cronbach's alpha ranged from 0.671-0.959) through the factor analysis, accounting for 71.21% of the total variance. Split-half reliability was 0.945. The greatest factors impacting physician's CR attitudes were inconvenience of the referral process (3.93 ± 0.65/5); lack of standard referral forms (3.92 ± 0.66), perceiving referral as the responsibility of another clinician (3.89 ± 0.67), and need for support in completing the referral form (3.89 ± 0.64). CONCLUSIONS/SIGNIFICANCE: The reliability, as well as content, face, cross-cultural, and structural validity of the 18-item, 4-subscale PACRR-C, were supported.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".