National Immunization Technical Advisory Groups (NITAGs): A schema for evaluating and comparing foundation instruments and NITAG operations
Bibliographic record
Abstract
The individual and community health benefits of vaccination have received significant attention and are now well understood. However, much less is known about immunization as a regulated space, its principles and standards and its institutions and instruments. In 2011, the World Health Organization (WHO) recommended that National Immunization Technical Advisory Groups (NITAGs) be established in each member country. NITAGSs are envisioned as independent, multidisciplinary expert groups within the national immunization framework, tasked with providing evidence-based evaluations and recommendations to governmental decision-makers about specific vaccines, vaccine-dosing, vaccine program development and immunization policy and practice more generally. As of 2020, 171 WHO countries have formed NITAGs. The widespread formation of NITAGs has highlighted an absence of sustained scholarship around immunization as a policy area subject to law, and it has given rise to many governance and operational questions. In 2017, for example, representatives of the Global NITAG Network (GNN) agreed that there is insufficient understanding of the impact of law on the functioning of NITAGs. Similarly, the Strategic Advisory Group of Experts on Immunization called for research into the variety of ways in which legislation and regulation have been used to promote immunization at a national level and to achieve different ends in relation to immunization and NITAG functioning. In answer to this call, the NITAG Environmental Scan (Project) was initiated. Drawing on scholarship around good governance, this article offers a comprehensive common assessment schema for critically and systematically approaching questions about NITAG governance and operation, applying that schema to the foundation instrument of the Côte d’Ivoire’s NITAG. It also reports on how well the schema is engaged by the NITAG foundation instruments in other GNN countries.
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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 | 0.198 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.024 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".