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Record W2495728017 · doi:10.1108/ijhg-02-2016-0014

Patient speaking for patients

2016· article· en· W2495728017 on OpenAlexaff
Alan Cassels

Bibliographic record

VenueInternational Journal of Health Governance · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarmOriginalityValue (mathematics)NarrativePublic relationsBusinessProcess (computing)MarketingLaw and economicsPolitical scienceEconomicsComputer scienceLawCreativity

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to recognize the vital reasons for including public and patient voices in health policy decision-making, but illustrates the challenge it creates for decision-makers who must consider whether those voices represent patient interests or corporate interests. Design/methodology/approach – This paper takes the form of a narrative review. Findings – The history of flibanserin, a controversial new drug to treat a debatable condition, illustrates how a public relations campaign could circumvent the well-established process to weigh evidence of potential harm vs benefit by one of the most robust drug regulators in the world. Practical implications – It is both vital to recognize a fundamental problem that exists when corporate interests deceptively assume the mantle of “the patient voice” and then act to reduce that influence while supporting and building capacity in genuinely independent, consumer-focused activities. Originality/value – This paper suggests that organizations interested in consumer protection and the safe and cost-effective use of health resources create policies and procedures that can foster genuine consumer involvement while recognizing the danger to patient safety and consumer interests when consumer involvement is hijacked by vested interests.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.325
GPT teacher head0.563
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2016
Admission routes1
Has abstractyes

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