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Record W2893932389 · doi:10.1016/j.vaccine.2018.06.012

Challenges to sustainable immunization systems in Gavi transitioning countries

2018· review· en· W2893932389 on OpenAlexaff
Tania Cernuschi, Stephanie Gaglione, Fiammetta Bozzani

Bibliographic record

VenueVaccine · 2018
Typereview
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversity of Toronto
FundersWorld Health Organization
KeywordsImmunizationBusinessDeveloping countryVaccinationLeast Developed CountriesEconomic growthAllianceSustainabilityGlobal healthPolitical scienceMedicineEconomicsHealth careImmunology

Abstract

fetched live from OpenAlex

The Global Vaccine Action Plan 2011-2020 (GVAP) aims to extend the full benefit of vaccination against vaccine-preventable diseases to all individuals. More than halfway through the Decade of Vaccines, countries classified as Middle-Income by the World Bank struggle to achieve several GVAP targets. Countries transitioning from Gavi, the Vaccine Alliance, represent a key sub-group of Middle Income Countries. Through a review of available literature on the subject, this study documents the lack of comparative analyses on immunization system performance in countries transitioning from Gavi support. Despite increased emphasis on the importance of programmatic sustainability beyond financing through the Gavi 2016-2020 Strategy and availability of data, existing literature has predominantly documented challenges related to domestic financing of immunization. This study complements a review of current literature with an analysis of country assessments conducted by immunization partners since 2011, in an effort to document programmatic challenges related to decision-making for immunization policy, delivery of services, and access to affordable and timely supply in Gavi transitioning countries. In light of the findings, we suggest continued systematic compilation of country performance data beyond financing to inform policy-making, in particular for: (i) development of a more nuanced theory of change towards sustainable immunization programmes and (ii) measurement of progress and key areas for attention and investment.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.046
GPT teacher head0.342
Teacher spread0.296 · 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 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

Citations42
Published2018
Admission routes1
Has abstractyes

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