Comparison of patients’ perceptions of family physicians’ patient-centeredness between virtual and in-person clinical encounters: A cross-sectional study
Bibliographic record
Abstract
Introduction: A clinician's patient-centeredness is a core construct of quality healthcare and is associated with several positive patient outcomes. This study aimed to compare patient-perceived patient centeredness between in-person and virtual clinical encounters during the coronavirus pandemic. Materials and Methods: Participants completed an online anonymous questionnaire pertaining to a recent clinical encounter. Patients of an academic family medicine teaching clinic scheduled for either an in-person or a virtual clinical encounter were recruited by phone over a two-month period. Using the patient-centered clinical method as a conceptual framework, patient-perceived patient centeredness was measured by the Patient-Perceived Patient-Centeredness Questionnaire-Revised (PPPC-R), consisting of 18 items that reflect three factors (healthcare process, context and relationship, and roles). Results: The sample consisted of 72 participants. There was no difference in the PPPC-R scores between participants who received in-person and those who received virtual care. However, the mean ranks for the PPPC-R total score and for all three factors were higher for participants who saw a family physician compared to participants who saw a family medicine learner. Conclusion: Family physicians provided similar quality healthcare, measured through a patient-perceived patient-centeredness lens, via both virtual and in-person appointments. These results support sustaining virtual care when deemed appropriate by both patient and clinician.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".