Primary Care Performance Measurement and Reporting at a Regional Level: Could a Matrix Approach Provide Actionable Information for Policy Makers and Clinicians?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Primary care services form the foundation of modern healthcare systems, yet the breadth and complexity of services and diversity of patient populations may present challenges for creating comprehensive primary care information systems. Our objective is to develop regional-level information on the performance of primary care in Canada. METHODS: A scoping review was conducted to identify existing initiatives in primary care performance measurement and reporting across 11 countries. The results of this review were used by our international team of primary care researchers and clinicians to propose an approach for regional-level primary care reporting. RESULTS: We found a gap between conceptual primary care performance measurement frameworks in the peer-reviewed literature and real-world primary care performance measurement and reporting activities. We did not find a conceptual framework or analytic approach that could readily form the foundation of a regional-level primary care information system. Therefore, we propose an approach to reporting comprehensive and actionable performance information according to widely accepted core domains of primary care as well as different patient population groups. CONCLUSIONS: An approach that bridges the gap between conceptual frameworks and real-world performance measurement and reporting initiatives could address some of the potential pitfalls of existing ways of presenting performance information (i.e., by single diseases or by age). This approach could produce meaningful and actionable information on the quality of primary care services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it