Doctors and Canadian Medicare: Improving System Performance Requires System Change
Bibliographic record
Abstract
Many of the issues raised and insights provided by Marchildon and Sherar (2018) in their essay on doctors and Canadian medicare are on target. The inadequacy of available data on physician payment, however, calls into question the robustness of some interprovincial comparisons, and when it comes to compensation, comparisons to US physicians would be most relevant. In contrast to their assertion of a steadily increasing growth rate in physician expenditure, a more recent and longer view shows historically low growth in the past few years. Furthermore, the blame assigned to physicians and their medical associations needs to be shared with governments and most of all could be attributed to the lack of system structures and supports for improvement. New governance arrangements at the group or regional levels are needed but are insufficient in themselves. The additional features embodied in the Patient's Medical Home are essential for advancing primary care. Going even further, full population registration, greater availability of alternate payment arrangements, active participation of physicians in healthcare administration and support for meaningful measurement and feedback loops are among the changes required to transform Canadian medicare.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.051 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 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".