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Record W2145769384 · doi:10.1186/1744-8603-8-24

Models for financing the regulation of pharmaceutical promotion

2012· article· en· W2145769384 on OpenAlex
Joel Lexchin

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.

Bibliographic record

VenueGlobalization and Health · 2012
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity Health NetworkYork UniversityUniversity of Toronto
Fundersnot available
KeywordsPromotion (chess)RevenueFinanceBusinessOrder (exchange)Public financePublic economicsStrengths and weaknessesHealth policyEconomicsHealth careEconomic growth

Abstract

fetched live from OpenAlex

Pharmaceutical companies spend huge sums promoting their products whereas regulation of promotional activities is typically underfinanced. Any option for financing the monitoring and regulation of promotion should adhere to three basic principles: stability, predictability and lack of (perverse) ties between the level of financing and performance. This paper explores the strengths and weaknesses of six different models. All these six models considered here have positive and negative features and none may necessarily be ideal in any particular country. Different countries may choose to utilize a combination of two or more of these models in order to raise sufficient revenue. Financing of regulation of drug promotion should more than pay for itself through the prevention of unnecessary drug costs and the avoidance of adverse health effects due to inappropriate prescribing. However, it involves an initial outlay of money that is currently not being spent and many national governments, in both rich and poor countries, are unwilling to incur extra costs.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.606
GPT teacher head0.588
Teacher spread0.018 · 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