Payment mechanism and GP self-selection: capitation versus fee for service
Bibliographic record
Abstract
This paper analyzes the consequences of allowing gatekeeping general practitioners (GPs) to select their payment mechanism. We model GPs’ behavior under the most common payment schemes (capitation and fee for service) and when GPs can select one among them. Our analysis considers GP heterogeneity in terms of both ability and concern for their patients’ health. We show that when the costs of wasteful referrals to costly specialized care are relatively high, fee for service payments are optimal to maximize the expected patients’ health net of treatment costs. Conversely, when the losses associated with failed referrals of severely ill patients are relatively high, we show that either GPs’ self-selection of a payment form or capitation is optimal. Last, we extend our analysis to endogenous effort and to competition among GPs. In both cases, we show that self-selection is never optimal. Copyright Springer Science+Business Media New York 2014(This abstract was borrowed from another version of this item.)
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".