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First do no harm – The impact of financial incentives on dental X-rays

2017· article· en· W2606015445 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Health Economics · 2017
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsnot available
FundersSyddansk UniversitetMcMaster UniversityHarvard School of Dental Medicine
KeywordsSalaryRemunerationPaymentFee-for-serviceHarmIncentiveBusinessFinanceService (business)Actuarial scienceMedicineEconomicsHealth carePsychologyMarketing

Abstract

fetched live from OpenAlex

This article assesses the impact of dentist remuneration on the incidence of potentially harmful dental X-rays. We use unique panel data which provide details of 1.3 million treatment claims by Scottish NHS dentists made between 1998 and 2007. Controlling for unobserved heterogeneity of both patients and dentists we estimate a series of fixed-effects models that are informed by a theoretical model of X-ray delivery and identify the effects on dental X-raying of dentists moving from a fixed salary to fee-for-service and patients moving from co-payment to exemption. We establish that there are significant increases in X-rays when dentists receive fee-for-service rather than salary payments and when patients are made exempt from payment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

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

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 head0.052
GPT teacher head0.396
Teacher spread0.344 · 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