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Record W4392973025 · doi:10.26443/mjgh.v9i1.1325

Did Dengvaxia-associated deaths result in an increase in vaccine hesitancy in the Philippines?

2020· article· en· W4392973025 on OpenAlexaff
Karim Atassi, Nicole Cifelli, Maddie Clark, Leen Makki, Janine Xu, Mercedes Yanes Lane

Bibliographic record

VenueMcGill Journal of Global Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsOutrageDistrustMeaslesVaccinationMedicinePublic healthMeasles vaccineEnvironmental healthEbola vaccineEconomic growthFamily medicinePolitical scienceImmunologyOutbreakVirologyEbola virusNursingPoliticsEconomics

Abstract

fetched live from OpenAlex

The development of the Dengvaxia vaccine and the subsequent vaccination campaign of 2016 in the Philippines proved to be an outstanding failure. This case study focuses on the impact of the vaccination campaign, which had a goal of vaccinating one million schoolchildren, ultimately reaching 830 000 students. Sanof Pasteur’s failure to adequately warn the Filipino public about Dengvaxia’s effect on antibody-dependent enhancement (ADE), coupled with rushing the implementation of the program by the Department of Health, ultimately led to the shutdown of the campaign in 2017. Therefore, we predict that the media sensationalization of the campaign, which created a public outrage, led to distrust of the healthcare system and vaccine hesitancy as well as an increase in vaccine-preventable diseases such as measles in the Philippines.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.057
GPT teacher head0.391
Teacher spread0.334 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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