National Immunization Technical Advisory Groups (NITAGs): A schema for evaluating and comparing foundation instruments and NITAG operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it