Balancing patient priorities for technical and interactional aspects of care in a measure of primary care quality
Bibliographic record
Abstract
AIM: This study attempts to strike a balance to measure primary care quality in a way that considers what is important to patients, providers and the healthcare system, all at the same time. BACKGROUND: The interest in delivering patient-centered primary care implies a need for patient-centered performance measurement. However, the distinction between measures of patient experience and technical aspects of care raises an unanswerable question: if a provider has good performance on technical measures but not on patient experience measures (or vice versa), what can be said about the quality of care? METHODS: We surveyed patients to determine the relative priorities of each of a series of primary care measures in the patients' relationship with their primary care provider. The on-line survey was co-designed with patient co-investigators. The items consisted of 14 primary care quality measures used in pre-existing performance report, 41 additional indicators including a novel set of patient-generated Key Performance Indicators and 17 questions about patients' demographics, health and socioeconomic status as well as open-ended questions. FINDINGS: Despite challenges, the study suggests that this is feasible. We argue that it is necessary to get better at measuring and finding ever-better ways to put patients at the center of primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".