Monitoring and Evaluation of National Vaccination Implementation: A Scoping Review of How Frameworks and Indicators Are Used in the Public Health Literature
Bibliographic record
Abstract
An effective Monitoring and Evaluation (M&E) framework helps vaccination programme managers determine progress and effectiveness for agreed indicators against clear benchmarks and targets. We aimed to identify the literature on M&E frameworks and indicators used in national vaccination programmes and synthesise approaches and lessons to inform development of future frameworks. We conducted a scoping review using Arksey and O'Malley's six-stage framework to identify and synthesise sources on monitoring or evaluation of national vaccination implementation that described a framework or indicators. The findings were summarised thematically. We included 43 eligible sources of 4291 screened. Most (95%) were in English and discussed high-income (51%) or middle-income (30%) settings, with 13 in Europe (30%), 10 in Asia-Pacific (23%), nine in Africa (21%), and eight in the Americas (19%), respectively, while three crossed regions. Only five (12%) specified the use of an M&E framework. Most (32/43; 74%) explicitly or implicitly included vaccine coverage indicators, followed by 12 including operational (28%), five including clinical (12%), and two including cost indicators (5%). The use of M&E frameworks was seldom explicit or clearly defined in our sources, with indicators rarely fully defined or benchmarked against targets. Sources focused on ways to improve vaccination programmes without explicitly considering ways to improve assessment. Literature on M&E framework and indicator use in national vaccination programmes is limited and focused on routine childhood vaccination. Therefore, documentation of more experiences and lessons is needed to better inform vaccination M&E beyond childhood.
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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 | 0.181 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.039 | 0.049 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".