Framework for the Design of Physician Remuneration Methods in Primary Health Care
Bibliographic record
Abstract
Economists have generated a large body of theoretical and empirical knowledge with respect to the design of physician remuneration methods (PRM). This knowledge is difficult to use for a policy maker, because of its technical nature and its fragmentation. The article brings together the scattered elements of theory and evidence into a structured framework that adds practical use value to economic theory, useful in the applied practice of policy development, design, implementation, and evaluation. The article argues that the optimal choice of PRM depends on the goals of the health care system, and on external contextual factors. Fee-for-service payments are best when the goals are quantity of care and risk acceptance. Capitation is best when the goals are collaboration between providers and delivery of preventive services and health promotion. Salaries are best when population density is low, and the goal is to recruit physicians to rural and remote areas. Blended payment models are recommended for the achievement of multiple goals. As a demonstration of use value, the framework is applied to the assessment of Canadian PRM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".