Strengthening and sustainability of national immunization technical advisory groups (NITAGs) globally: Lessons and recommendations from the founding meeting of the global NITAG network
Bibliographic record
Abstract
National Immunization Technical Advisory Groups (NITAGs) provide independent, evidence-informed advice to assist their governments in immunization policy formation. However, many NITAGs face challenges in fulfilling their roles. Hence the many requests for formation of a network linking NITAGs together so they can learn from each other. To address this request, the Health Policy and Institutional Development (HPID) Center (a WHO Collaborating Center at the Agence de Médecine Préventive - AMP), in collaboration with WHO, organized a meeting in Veyrier-du-Lac, France, on 11 and 12 May 2016, to establish a Global NITAG Network (GNN). The meeting focused on two areas: the requirements for (a) the establishment of a global NITAG collaborative network; and (b) the global assessment/evaluation of the performance of NITAGs. 35 participants from 26 countries reviewed the proposed GNN framework documents and NITAG performance evaluation. Participants recommended that a GNN should be established, agreed on its governance, function, scope and a proposed work plan as well as setting a framework for NITAG evaluation.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".