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Record W2345649934 · doi:10.1080/23762004.2016.1178563

Polio Eradication and Health Systems in Karachi: Vaccine Refusals in Context

2015· article· en· W2345649934 on OpenAlexaff
Svea Closser, Rashid Jooma, Emma Varley, Naina Qayyum, Sonia Rodrigues, Akasha Sarwar, Patricia A. Omidian

Bibliographic record

VenueGlobal Health Communication · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsBrandon University
FundersNorthwestern UniversityBill and Melinda Gates FoundationUnited States Agency for International Development
KeywordsPoliomyelitisPoliomyelitis eradicationContext (archaeology)Polio vaccineGovernment (linguistics)SanitationDistrustMedicineFocus groupPoliovirusPublic healthPolio VaccinationPopulationEconomic growthEnvironmental healthSocioeconomicsPolitical scienceGeographyBusinessVirologyNursingSociology

Abstract

fetched live from OpenAlex

Community and health worker engagement will be key to polio eradication in Karachi, Pakistan. In this study, the authors conducted participant observation, interviews, and a document review in SITE Town, Karachi, an area that in recent years has harbored poliovirus. SITE’s diverse population includes large numbers of internally displaced persons who are disproportionately affected by polio and are more likely than other populations to refuse the polio vaccine. Vaccine acceptance and worker motivation in SITE Town were shaped by the discrepancy in funding and attention for polio eradication campaigns as compared with routine services. Parental vaccine refusals stemmed from a distrust of government and international actors that provided few services but administered polio vaccine door-to-door every month. Addressing this discrepancy could therefore be key to eliminating polio. The authors suggest short-term improvements to routine immunization and sanitation in key polio endemic areas, coupled with a long-term focus on sustainable improvements to routine immunization and broader health services.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.075
GPT teacher head0.423
Teacher spread0.348 · 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 designObservational
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

Citations11
Published2015
Admission routes1
Has abstractyes

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