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Record W2376655827 · doi:10.3138/cjh.ach.51.1.rev33

<i>Vaccine Nation: America's Changing Relationship with Immunization</i> by Elena Conis

2016· article· en· W2376655827 on OpenAlexaffvenue
Catherine Carstairs

Bibliographic record

VenueJournal of History · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVaccinationMedicineMeaslesPoliomyelitisImmunizationPolio vaccineSmallpoxPublic healthMeasles vaccinePolitical scienceEconomic growthFamily medicineImmunologyPediatricsEconomics

Abstract

fetched live from OpenAlex

Vaccine Nation: America's Changing Relationship with Immunization, by Elena Conis. Chicago, The University of Chicago Press, 2015. 353 pp. $27.50 US (cloth), $18.00 (paper). Given extensive news reports about parents refusing to vaccinate their children, it might seem surprising that American children actually receive more vaccinations than ever before. Less than 0.5 percent of children receive no vaccines at all. In this smart and balanced book, Conis argues that widespread adherence to vaccination is at least as worthy of study as vaccine resistance. She turns her lens on the proliferation of vaccines in the postwar era when new vaccines targeted the milder diseases of childhood such as measles, mumps, and whopping cough. More recently, vaccines have been developed and promoted that protect against diseases that largely affect adults (HPV and Hepatitis B), but are given predominantly to children. Through a careful examination of vaccine development, federal vaccine policies, and the public debate over vaccination, Conis demonstrates why health policy-makers have promoted vaccination and why children receive most of the vaccinations. In doing so, she helps us to understand the growing resistance to vaccination. Conis argues that vaccines came to be seen as an important public good in the wake of the polio epidemics of the middle decades of the twentieth century. The timing of this might be slightly off: toxoid had already had a dramatic impact on reducing deaths from diphtheria. She argues that the rise of pediatrics as a medical specialty and the array of public health services provided to children ensured that children were more easily accessed than other segments of the population and that as a result, children became the primary focus of vaccination. Public funding for vaccinations increased in the post-war era not only because policy-makers realized that the vaccines could dramatically reduce disease but also because after the introduction of the polio and measles vaccines, it became clear that these diseases were increasingly concentrated in poorer neighbourhoods and among African American and Hispanic populations. Vaccines were a relatively cheap option for reducing health disparities. At the same time, new vaccines changed perceptions of childhood illnesses. Measles had once been dismissed as an inconvenience, but vaccine promoters emphasized that measles could have dangerous side-effects including encephalitis, deafness, and brain damage. Mumps was similarly transformed from a mild disease of childhood to one with frightening complications including male sterility and mental retardation. Conis argues that the introduction of the mumps, measles, and rubella (MMR) vaccine by Merck in 1971 ensured that American children would be vaccinated against the mumps, but she does not believe that a profit-seeking pharmaceutical company duped Americans into vaccinating their children. Instead, she argues that concern about the declining birth rate made the possible complications of the mumps seem more serious. …

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0060.008
Open science0.0010.002
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0370.021

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2016
Admission routes2
Has abstractyes

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