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Record W2075534100 · doi:10.1258/135581903322403317

The economics of direct-to-consumer advertising of prescription-only drugs: prescribed to improve consumer welfare?

2003· article· en· W2075534100 on OpenAlexaff
Steven G. Morgan, Barbara Mintzes

Bibliographic record

VenueJournal of Health Services Research & Policy · 2003
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsMedical prescriptionDirect-to-consumer advertisingPrescription drugAdvertisingWelfareConsumer welfareBusinessMarketingFunction (biology)DrugEconomicsMedicinePharmacology

Abstract

fetched live from OpenAlex

According to economic theory, one might expect that the informational content of direct-to-consumer advertising of prescription-only drugs would improve consumers' welfare. However, contrasting the models of consumer and market behaviour underlying this theory with the realities of the prescription-only drug market reveals that this market is distinct in ways that render it unlikely that advertising will serve an unbiased and strictly informative function. A review of qualitative evidence regarding the informational content of drug advertising supports this conclusion. Direct-to-consumer prescription drug advertising concentrates on particular products, and features of those products, to the exclusion of others, and the information provided has frequently been found to be biased or misleading in regulatory and academic evaluations. Governments that have so far resisted direct-to-consumer advertising should invest in independent sources of evidence that could help consumers and professionals to better understand the risks and benefits of treating disease with alternative drug and non-drug therapies, rather than permitting direct-to-consumer prescription drug advertising.

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)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.230
GPT teacher head0.562
Teacher spread0.332 · 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 teacher head, not a consensus.

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

Citations19
Published2003
Admission routes1
Has abstractyes

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