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Record W2795401606 · doi:10.1002/hast.838

The Purchased Patient Advocate

2018· article· en· W2795401606 on OpenAlexaboutno aff
Carl Elliott

Bibliographic record

VenueThe Hastings Center Report · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsVictoryPower (physics)Conflict of interestQuackerySkepticismSecrecyPatient advocacyPublic relationsLanguage changeLawPolitical scienceSociologyBusinessMedicineAlternative medicinePoliticsMEDLINE

Abstract

fetched live from OpenAlex

Abstract Thirty years ago, the only people drug companies thought worth buying were doctors and politicians. But the ground began to shift in the 1980s, when HIV/AIDS activists showed everyone how powerful patient advocates could be. It didn't hurt that many advocates were so strapped for money that they could be purchased at bargain prices. Today over 80 percent of patient advocacy groups accept money from the pharmaceutical industry, and the testimony of marginalized patients carries such cultural power that drug companies like Sprout Pharmaceuticals are willing to fake grassroots patient movements. Whether you see this change as a victory for patients or a cautionary tale of institutional corruption depends on how deeply committed you are to the idea that free markets represent our best hope for the future of medicine. Count Sharon Batt as one of the skeptics. Her superb new book, Health Advocacy, Inc.: How Pharmaceutical Funding Changed the Breast Cancer Movement, is a deep scholarly account of the way that pharmaceutical funding has warped the patient advocacy movement into a tool for medical capitalism. Taking the Canadian breast cancer movement as a case study, Batt uses extensive interviews, scholarly resources, and her personal history to explore the struggles faced by patient advocates as they decide whether industry funding is necessary to keep their organizations afloat .

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.427
GPT teacher head0.545
Teacher spread0.118 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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