Measuring the Performance of Primary Healthcare: Existing Capacity and Potential Information to Support Population-Based Analyses
Bibliographic record
Abstract
WHAT DID WE DO?: We reviewed the degree to which existing population-based data in Canada can be used to describe and report on primary healthcare (PHC) performance. We identified gaps in current data sources and made recommendations on how these gaps might be addressed to support quality improvement and public reporting for PHC. WHAT DID WE LEARN?: Population-based survey and administrative data are available to describe population characteristics and other contextual factors for PHC, as well as some aspects of the material, financial and human resources inputs, and selected activities and decisions at the policy, management and clinical levels. Existing data can also be used to describe some volumes and types of PHC outputs. However, we currently have limited population-based data to assess selected qualities of PHC services (e.g., coordination and interpersonal effectiveness) and most immediate outcomes of PHC. The ability to link data to assess outcomes and attribute changes in outcomes to PHC is limited. A full report describing more than 130 indicators from existing data sources and gaps in current data is available at www.chspr.ubc.ca. WHAT ARE THE IMPLICATIONS?: As we look to the future, there is a clear need to build on existing data sources to expand PHC data capacity in Canada. Data are needed to inform an understanding of PHC outputs, outcomes and the linkages among PHC dimensions. Commitment to a comprehensive PHC data collection strategy and information system is needed across Canadian provinces and territories to inform policy development and planning, to evaluate PHC redesign initiatives and to meet the accountability expectations of Canadians.
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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 | 0.348 | 0.541 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.023 | 0.041 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".