How the Novel Person-Centered Primary Care Measure Performs in Canada
Bibliographic record
Abstract
Background: The Person-Centered Primary Care Measure (PCPCM) is a relatively new and concise yet comprehensive measure of primary care quality. The objectives of this study are to administer the PCPCM in Canada and to understand whether there is an association between the PCPCM and sociodemographic and patient experience measures. Methods: The PCPCM was added to the routine patient experience survey administered at a multi-site academic primary care practice in Toronto, Canada. The survey was administered to patients with an e-mail on file and included questions on demographics, timely access, patient-centeredness, care continuity, and the PCPCM. Descriptive statistics were used to summarize the PCPCM. We used 1-way analysis of variance to determine whether there was an association between the PCPCM and patient demographics and patient experience measures at the team level. Results: We analyzed 2581 survey responses. The mean PCPCM score was 3.47. The PCPCM was higher for people with better health status (P < .001), those born in Canada (P = .036), those with higher educational attainment (P = .003), and those who knew their provider for longer (P < .001). There was no significant association between PCPCM and income quintile (P = .417). The PCPCM was significantly associated with all 9 patient experience measures related to access, patient-centeredness, and care continuity (P < .001). Conclusions: The 11-item PCPCM is a feasible and meaningful measure that reflects patient-reported access, continuity, and patient-centeredness and can be incorporated into primary care patient experience surveys to evaluate and improve quality of care.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".