Measuring Patients’ Perceptions of Health Care Encounters: Examining the Factor Structure of the Revised Patient Perception of Patient-Centeredness (PPPC-R) Questionnaire
Bibliographic record
Abstract
Purpose: Given the ongoing desire to make health care more patient-centered and growing evidence supporting the provision of patient-centered care, it is important to have valid tools for measuring patient-centered care. The patient-centered clinical method (PCCM) is a conceptual framework for providing patient-centered care. A revision to the PCCM framework led to a corresponding need to enhance the Patient Perception of Patient-Centeredness (PPPC) questionnaire. The original PPPC was aligned with the components of the PCCM conceptual framework and developed to measure patient-centeredness from the patient’s perspective. The purpose of this study was to examine the factor structure of a revised version of the PPPC (ie, PPPC-R). Methods: Eleven new items were added to the original 14 items. The modified questionnaire was administered to patients in primary health care teams in Ontario, Canada. The confirmatory factor analysis was conducted on a subset of 381 patients who had seen a family physician. Results: The initial proposed 4-factor model first tested with a confirmatory factor analysis (CFA) did not fit adequately. Exploratory factor analysis was therefore used as a second step to modify the model and to identify weak items. A 3-factor exploratory model with 18 of the original 25 items was converted into a final hypothetical CFA model that had a good fit (χ2(132) = 176.795, P < 0.01; CFI = 0.991; RMSEA = 0.030). The third factor contained only 2 items and so is interpreted with caution. Conclusions: The validity of the PPPC-R is supported by some congruence between the conceptual framework (the PCCM) and the statistical analysis (CFA), but there is not a 1:1 correspondence. The components of the PCCM represent conceptually what is important when teaching, researching, and providing patient-centered care, whereas the PPPC-R represents patient-centered care as it is experienced by the patient.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".