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Record W2104388552 · doi:10.12927/hcpol.2013.23466

Coalition Priorité Cancer and the Pharmaceutical Industry in Québec: Conflicts of Interest in the Reimbursement of Expensive Cancer Drugs?

2013· article· fr· W2104388552 on OpenAlexafffundvenueabout
David Hughes, Bryn Williams–Jones

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languagefr
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec-Société et Culture
KeywordsReimbursementContext (archaeology)Conflict of interestBusinessDrug pricingPharmaceutical industryPublic interestPublic relationsPolitical scienceActuarial scienceMedicineFinanceLawPharmacologyHealth care

Abstract

fetched live from OpenAlex

In the context of scarce public resources, patient interest groups have increasingly turned to private organizations for financing, including the pharmaceutical industry. This practice puts advocacy groups in a situation of potential conflicts between the interests of patients and those of the drug companies. The interests of patients and industry can converge on issues related to the approval and reimbursement of medications. But even on this issue, interests do not always align perfectly. Using the Quebec example of Coalition Priorité Cancer (CPC) as a case study, we examine the ethical issues raised by such financial relationships in the context of drug reimbursement decision-making. We collected, compiled and analyzed publicly available information on the CPC's organization and activities; this approach allowed us to raise and discuss important questions regarding the possible influence exerted on patient groups by donors. We conclude with some recommendations.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0020.000

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 head0.532
GPT teacher head0.584
Teacher spread0.052 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations15
Published2013
Admission routes4
Has abstractyes

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