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Record W1532073654 · doi:10.1257/pol.20140160

Physicians Treating Physicians: Information and Incentives in Childbirth

2016· article· en· W1532073654 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAmerican Economic Journal Economic Policy · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of British ColumbiaCanadian Institute for Advanced Research
FundersUniversity of MichiganRobert Wood Johnson Foundation
KeywordsIncentiveMicrodata (statistics)Quarter (Canadian coin)ChildbirthBusinessFamily medicineMedicinePregnancyEconomicsEnvironmental health

Abstract

fetched live from OpenAlex

This paper provides new evidence on the interaction between patient information and physician financial incentives. Using rich microdata on childbirth, we compare the treatment of physicians when they are patients with that of comparable nonphysicians. We also exploit the presence of HMO-owned hospitals to determine how the treatment gap varies with providers’ financial incentives. Consistent with induced demand, physicians are approximately 10 percent less likely to receive a C-section, with only a quarter of this effect attributable to differential sorting. While financial incentives affect the treatment of nonphysicians, physician-patients are largely unaffected. Physicians also have better health outcomes. (JEL D83, I11, J16, J44)

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.011
GPT teacher head0.248
Teacher spread0.237 · 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