Innovation in Physician Remuneration in France: What Lessons for Canada?
Bibliographic record
Abstract
During the past decade, France has experienced two major reforms in remuneration models for general practitioners who work outside public health care organizations: Remuneration for Public Health Objectives (Rémunération sur Objectifs de Santé Publique-ROSP ) and Experiments with New Models of Remuneration (Expérimentations des Nouveaux Modes de Rémunération-ENMR).These two initiatives introduced payments based on performance in the areas of quality of care, organization of services and multidisciplinary practice.In the first model, individual physicians receive incentives for preventive practices, use of generics and improvements in work organization.In the second model, incentives are provided to multi-professional practice groups to foster interdisciplinary collaboration and patient involvement.While French general practitioners accustomed to fee-for-service remuneration were at first reluctant to accept a mixed remuneration model, they eventually came to embrace it.The ROSP has significantly improved targeted areas of practice, although it has had less impact on preventive practices than on use of generics and work organization.The ENMR has helped formalize inter-professional relationships in primary care and has thus contributed to team integration.These "experiments" suggest that a deliberate distinction between changes to individual physician payment and changes to how multi-professional practice groups are paid and practice may be a good starting point when introducing financial incentives to enable benefits and avoid negative consequences.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".