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Record W2891256888 · doi:10.1002/hec.2971

PHYSICIAN RESPONSE TO PAY-FOR-PERFORMANCE: EVIDENCE FROM A NATURAL EXPERIMENT

2013· preprint· en· W2891256888 on OpenAlexaffabout
Jinhu Li, Jeremiah Hurley, Philip DeCicca, Gioia Buckley

Bibliographic record

VenueHealth Economics · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNatural experimentIncentivePay for performanceSelection biasHealth carePsychological interventionPublic economicsRobustness (evolution)Actuarial scienceBusinessQuality (philosophy)ConfoundingPopulationMedicineNursingEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

This study exploits a natural experiment in the province of Ontario, Canada, to identify the impact of pay-for-performance (P4P) incentives on the provision of targeted primary care services and whether physicians' responses differ by age, size of patient population, and baseline compliance level. We use administrative data that cover the full population of Ontario and nearly all the services provided by primary care physicians. We employ a difference-in-differences approach that controls for selection on observables and selection on unobservables that may cause estimation bias. We implement a set of robustness checks to control for confounding from other contemporaneous interventions of the primary care reform in Ontario. The results indicate that responses were modest and that physicians responded to the financial incentives for some services but not others. The results provide a cautionary message regarding the effectiveness of employing P4P to increase the quality of health care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.122
GPT teacher head0.341
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2013
Admission routes2
Has abstractyes

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