Evaluating primary care efficiency across Canadian provinces: A DEA-based approac
Bibliographic record
Abstract
This study evaluates the relative efficiency of primary care systems across Canadian provinces using Data Envelopment Analysis (DEA). Employing four DEA models—CCR, BCC input-oriented, BCC output-oriented, and additive—we assess performance based on three key inputs: average clinical payment per physician, physician density, and digital care uptake. The single output considered is average annual patient visits per adult, reflecting service utilization. Results reveal significant variation in efficiency scores across models, with the CCR method yielding lower scores due to its constant returns to scale assumption. In contrast, BCC and additive models identify more provinces as efficient, highlighting the impact of scale flexibility. Notably, Ontario and Nova Scotia consistently demonstrate high efficiency across all models, suggesting effective resource utilization and strong patient engagement. These findings offer valuable insights for policymakers aiming to optimize primary care delivery and support evidence-based resource allocation in Canada’s healthcare system.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".