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Record W4210574766 · doi:10.9745/ghsp-d-21-00310

Maintaining Polio-Free Status in Indonesia During the COVID-19 Pandemic

2022· article· en· W4210574766 on OpenAlexaff

Bibliographic record

VenueGlobal Health Science and Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutions123 Certification (Canada)
FundersWorld Health OrganizationMinistry of Health of the Republic of IndonesiaBill and Melinda Gates Foundation
KeywordsPandemicPoliomyelitisPoliomyelitis eradicationVaccinationImmunizationPolio VaccinationPolio vaccineHuman resourcesPublic health

Abstract

fetched live from OpenAlex

<h3>Key Messages</h3> Indonesia’s polio-free status as well as the 2023 global polio eradication target have been threatened by disruptions to immunization services caused by the coronavirus disease (COVID-19) pandemic and related restrictions. Fear of contracting COVID-19, human resource diversion, and travel restrictions posed barriers to delivering polio immunization services during the pandemic. To resume polio vaccination efforts and maintain polio-free status, Indonesia health authorities need to take action to: ∘Maximize vaccine service reach by mapping children who have not been vaccinated and monitor the vaccine supply chain ∘Integrate vaccine delivery with pandemic response efforts ∘Mobilize communities for advocacy and education ∘Adjust programs to address human resource, physical, and financial resource gaps ∘Strengthen monitoring and evaluation and surveillance efforts

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.474
Teacher spread0.389 · 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 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

Citations8
Published2022
Admission routes1
Has abstractyes

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