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Record W2099981452 · doi:10.1002/hpm.819

Regulating the Dutch pharmaceutical market: improving efficiency or controlling costs?

2005· article· en· W2099981452 on OpenAlexaff
Peter de Wolf, Werner Brouwer, Frans Rutten

Bibliographic record

VenueThe International Journal of Health Planning and Management · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsIncentiveGovernment (linguistics)BusinessCompetition (biology)Public economicsHealth careMedical prescriptionIndustrial organizationPharmaceutical industryMarket shareEconomicsMarketingMarket economyEconomic growthMedicinePharmacology

Abstract

fetched live from OpenAlex

In this paper, we describe the Dutch pharmaceutical market, which is heavily regulated by the government. Through the regulation of prices and promoting prudent use, the Dutch government tries to bring down the cost of pharmaceuticals, which increases every year at a higher rate than total health care expenditure. The complex system of regulation, especially aimed at cost containment, is not very effective, particularly with respect to controlling outpatient pharmaceutical expenditure. Moreover, the system has few incentives towards efficiency. Though the market share of generic pharmaceuticals is rapidly growing, pharmaceutical expenditure has not decreased accordingly. The discounts offered by wholesalers of generic products to pharmacists produce private rather than societal gains from generic prescriptions. Dismantling the current regulatory system, boosting competition and efficiency with insurers in a leading role, seems to be the way forward.

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)0.012
metaresearch head score (Gemma)0.034
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0100.015
Open science0.0030.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.078
GPT teacher head0.366
Teacher spread0.288 · 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

Citations31
Published2005
Admission routes1
Has abstractyes

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