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Record W2911717912 · doi:10.1177/1363459319829198

The empowered patient on a historical-rhetorical model: 19th-century patent-medicine ads and the 21st-century health subject

2019· article· en· W2911717912 on OpenAlexafffund
Judy Z. Segal

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2019
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSubject (documents)RhetoricPaternalismAssertionPower (physics)Rhetorical questionHealth careSociology of health and illnessMedical prescriptionSociologyMedicineAdvertisingLawBusinessPolitical scienceLiteratureArtNursing

Abstract

fetched live from OpenAlex

The contemporary health subject, often described as a new, empowered patient, is not simply a character in a story of progress toward knowledge and power, away from credulity and passivity. Before the 20th century, and the assertion of a medical system that became frankly paternalistic, laypeople adjudicated on many matters of illness and its treatments. That is, 18th- and 19th-century health subjects were empowered too, and studying them, especially as consumers of health products, helps us develop a more nuanced account of our current medico-commercial selves. Comparing historical advertisements for "patent medicines" and contemporary direct-to-consumer ads for prescription pharmaceuticals, this essay contributes to such an account. It identifies strategies that drug marketers have deployed over centuries to persuade consumers to buy their products, and it tracks a rhetoric of interpellation in advertisements that not only address but also constitute health subjects. The goal of the analysis is to increase alertness to our own susceptibilities to pharmaceutical ads and adjacent rhetorics of health and illness.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0180.001
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 head0.339
GPT teacher head0.557
Teacher spread0.218 · 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 designQualitative
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

Citations10
Published2019
Admission routes2
Has abstractyes

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Same venueHealth An Interdisciplinary Journal for the Social Study of Health Illness and MedicineSame topicPharmaceutical industry and healthcareFrench-language works237,207