Establishing a Primary Care Performance Measurement Framework for Ontario
Bibliographic record
Abstract
A systematic approach to Primary Care Performance Measurement is needed to provide useful information on a regular basis to inform planning, management and quality improvement at both the practice and system levels. Based on an environmental scan, a summit of primary care stakeholders and a stakeholder survey and supported by Measures and Technical Working Groups, the Ontario Primary Care Performance Measurement Steering Committee, representing 20 stakeholder organizations, identified system- and practice-level measurement priorities and related specific performance measures across nine domains of primary care performance. This initiative addressed measures' selection and technical specification. It did not include data collection. Lessons learned in Ontario can assist other jurisdictions developing frameworks for monitoring and reporting on primary care performance. Cross-country alignment could lead to a coordinated approach to measure and target areas for primary care performance improvement in Canada.
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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 | 0.062 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".