MétaCan
Menu
Back to cohort
Record W1554268507 · doi:10.1787/228632341330

The Remuneration of General Practitioners and Specialists in 14 OECD Countries

2008· paratext· en· W1554268507 on OpenAlexaboutno aff
Rie Fujisawa, Gaétan Lafortune

Bibliographic record

VenueOECD health working papers · 2008
Typeparatext
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsRemunerationPurchasing power parityPurchasing powerCurrencyWageLiberian dollarCzechBusinessDemographic economicsGeographyEconomicsExchange rateLabour economicsFinanceMonetary economics

Abstract

fetched live from OpenAlex

This paper provides a descriptive analysis of the remuneration of doctors in 14 OECD countries for which reasonably comparable data were available in OECD Health Data 2007 (Austria, Canada, the Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Luxembourg, Netherlands, Switzerland, the United Kingdom and the United States). Data are presented for general practitioners (GPs) and medical specialists separately, comparing remuneration levels across countries both on the basis of a common currency (US dollar, adjusted for purchasing power parity) and in relation to the average wage of all workers in each country.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.303
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations168
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueOECD health working papersSame topicHealthcare Policy and ManagementFrench-language works237,207