Arabic translation and psychometric validation of the revised Patient Perception of Patient-Centeredness (PPPC-R) questionnaire
Bibliographic record
Abstract
BACKGROUND: The Revised Patient Perception of Patient-Centeredness (PPPC-R) scale was originally formulated in English to examine patient-centeredness. OBJECTIVE: To translate and validate the PPPC-R questionnaire into Arabic. METHODOLOGY: Translating the PPPC-R into Arabic involved forward and back translations by bilingual experts. Content validity was checked through the Item and Scale Content Validity Indices. The instrument underwent pilot testing and was then completed by 179 patients in the emergency department to examine construct validity using both confirmatory and exploratory factor analyses. Reliability was tested using Cronbach’s alpha to ensure internal consistency. RESULTS: The Item Content Validity Index ranged between 0.66 and 1, and the Scale Content Validity Index was 0.96. Initial confirmatory factor analysis of the Arabic-PPPC-R revealed an inadequate fit, prompting an exploratory factor analysis with Promax rotation, which identified three factors that explained 66.3% of the variance. The refined model, tested again using confirmatory factor analysis, demonstrated an acceptable fit with improved statistical measures, including CFI and TLI values above 0.90 and RMSEA of 0.07. Reliability testing revealed high internal consistency with a Cronbach’s alpha of 0.949 for the full scale and between 0.889 and 0.906 for the individual subscales. CONCLUSION: The study findings showed that the Arabic version of the PPPC-R has good structural characteristics and is a reliable and valid instrument for measuring patient-centeredness.
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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 | 0.006 | 0.024 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".