Treatment and referral decisions under di¤erent physician payment mechanisms.
Bibliographic record
Abstract
This paper analyzes and compares the incentive properties of some common payment mechanisms for GPs, namely fee for service (FFS), capitation and fundholding. It focuses on gatekeeping GPs and it speci…cally recognizes GPs heterogeneity in both ability and altruism. It also allows inappropriate care by GPs to lead to more serious illnesses. The results are as follows. Capitation is the payment mechanism that induces the most referrals to expensive specialty care. Fundholding may induce almost as much referrals as capitation when the expected costs of GPs care are high relative to those of specialty care. Although driven by …nancial incentives of di¤erent nature, the strategic behaviours associated with fundholding and FFS are very much alike. Finally, whether a regulator should use one or another payment mechanism for GPs will depend on (i) his priorities (either cost-containment or quality enhancement) which, in turn, depend on the expected cost di¤erence between GPs care and specialty care, and (ii) the distribution of pro…les (diagnostic ability and altruism levels) among GPs. Léger thanks HEC Montréal, FQRSC and SSHRC for funding. Jelovac thanks Banque Nationale Belge for funding. We thank two anonymous referees as well as seminar and conference participants at the University
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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 | 0.021 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".