Contextualizing the Revised Patient Perception of Patient-Centeredness (PPPC-R) Scale in Primary Healthcare settings: a Validity and Reliability Evaluation Study
Bibliographic record
Abstract
Abstract Background The Patient Perception of Patient-Centeredness (PPPC) scale in English was recently revised, and it is necessary to test this instrument in different primary care populations. Aim This study aimed to assess the validity and reliability of a Chinese version of the PPPC scale. Design Delphi method was used to address the content validity of the PPPC scale by calculating the Content Validity Index, Content Validity Ratio, the adjusted Kappa, and the Item Impact Score. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) were used to assess the construct validity of the PPPC scale. The internal consistency was also assessed. Setting/participants A cross-sectional survey included 188 outpatients in Guangzhou city and 108 outpatients in Hohhot city from community health service centers or stations. Results The 21 items in the scale were relevant to the component they belong to. The Item-level Content Validity Index for each item was higher than 0.79, and the average Scale-level content validity index was 0.97 in each evaluation round. The initial proposed 4-factor CFA model did not fit adequately. Still, we found a 3-factor solution based on our EFA model and the validation via the CFA model (model fit:χ2=294.573, P<0.001, RMSEA=0.044, CFI=0.981; factor loadings: 0.553 to 0.888). Cronbach's α also indicated good internal consistency reliability: The overall Cronbach's α was 0.922, and the Cronbach's α for each factor was 0.851, 0.872, and 0.717, respectively. Conclusions The Chinese version of the PPPC scale provides a valuable tool for evaluating patient-centered medical service quality.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".