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Record W2115986688 · doi:10.5539/ijef.v4n11p24

Private Health Care and Drug Quality in Germany – A Game-Theoretical Approach

2012· article· en· W2115986688 on OpenAlexvenueno aff
Tristan Nguyen, Karsten Rohlf

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsStatutory lawBusinessCasualty insuranceGroup insuranceActuarial scienceHealth insuranceGeneral insuranceSelf-insuranceQuality (philosophy)GermanInsurance policyPublic economicsHealth careKey person insuranceInsurance lawEconomicsIncome protection insuranceEconomic growthLawPolitical science

Abstract

fetched live from OpenAlex

Quality of medical treatment is a major goal of Germany's statutory health insurance system. According to our game theoretical approach, existing price-discrimination between statutory and private health insurance leads to a higher quality of innovative drugs. Hence, a move into the direction of a single payer health care (so-called citizens’ insurance) should result in a reduction of innovative drugs' quality. Moreover, and in the case of citizens insurance's implementation, innovative drugs' price level should increase for patients with statutory health insurance. Furthermore, a similar effect is caused by the Act on the Reform of the Market for Medicinal Products (AMNOG) which leads to reduced prospects for price discriminations between the statutory and private health insurance system. In summary, the existence of private health insurance in Germany does not cause unfavourable cream-skimming. Rather the division of the German health care sector (statutory vs. private health insurance) results in higher drug quality at lower prices for patients with statutory health insurance.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.044
GPT teacher head0.325
Teacher spread0.281 · 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 designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

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