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Record W3026017398 · doi:10.1016/j.vaccine.2020.05.028

Global Vaccine Action Plan lessons learned III: Monitoring and evaluation/accountability framework

2020· article· en· W3026017398 on OpenAlexaff
Thomas Cherian, Angela Hwang, Carsten Mantel, Chantal Laroche Veira, Stefano Malvolti, Noni E. MacDonald, Christoph A. Steffen, Ian Jones, Alan R. Hinman

Bibliographic record

VenueVaccine · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsAccountabilityAction planStakeholderPublic relationsContext (archaeology)Civil societyProcess (computing)Political scienceAction (physics)Scope (computer science)BusinessPublic administrationProcess managementPoliticsEconomicsManagementComputer science

Abstract

fetched live from OpenAlex

INTRODUCTION: The Monitoring & Evaluation/Accountability (M&E/A) framework of the Global Vaccine Action Plan (GVAP) was used to report progress annually to the World Health Assembly (WHA). METHODS: Stakeholder feedback was obtained through five reviews consisting of surveys and semi-structured interviews conducted from 2017 to 2019. Participants consisted of individuals involved in the development and implementation of GVAP or its M&E/A process, national immunization managers, academics, representatives of non-governmental organizations, and civil society organizations. RESULTS: The feedback was mixed and contradictory for some components, though most participants reported that the M&E/A process was a highlight of GVAP and a step in the right direction. Several of the goals and targets were considered aspirational and unrealistic for many countries. There were mixed responses on whether it promoted accountability, especially at the country level. DISCUSSION: The mixed and contradictory views on the M&E/A processes and its impact suggested a failure of communication about its scope and intent. Though the process, especially the annual reporting to the WHA, kept immunization high on the global agenda, it failed to fully meet the expectations in promoting accountability. Engaging with countries to capture the local context in setting global goals and targets and promoting local M&E/A processes will be important to achieve accountability in the next decade.

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)0.001
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 score0.069
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.413
Teacher spread0.284 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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