Global Vaccine Action Plan lessons learned III: Monitoring and evaluation/accountability framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.195 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".