Moving forward on strengthening and sustaining National Immunization Technical Advisory Groups (NITAGs) globally: Recommendations from the 2nd global NITAG network meeting
Bibliographic record
Abstract
National Immunization Technical Advisory Groups (NITAGs) provide independent, evidence-informed advice to assist their governments in immunization policy formation. This is complex work and many NITAGs face challenges in fulfilling their roles. Inter-country NITAG collaboration opportunities have the potential to enhance NITAG function and grow the quality of recommendations. Hence the many requests for formation of a network linking NITAGs together so they can learn from each other. The first Global NITAG Network (GNN) meeting, held in 2016, led to a push to launch the GNN and grow the network. At the second GNN meeting, held June 28-29, 2017 in Berlin, the GNN was formally inaugurated. Participants discussed GNN governance, reflected on the April 2017 Strategic Advisory Group of Experts (SAGE) on Immunization conclusions concerning strengthening of NITAGs and also shared NITAG experiences in evaluation and inter-country collaborations and independence. They also discussed the role of Regional Technical Advisory Groups on Immunization (RTAGs) and regional networks. A number of issues were raised including NITAGs and communications, dissemination of recommendations and vaccine implementation as well as implications of off-label recommendations. Participants were alerted to immunization evidence assessment sites and value of sharing of resources. They also discussed potential GNN funding opportunities, developed an action plan for 2017-18 and selected a Steering Committee to help move the GNN forward. All participants agreed on the importance of the GNN and the value in attracting more countries to join the GNN.
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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 | 0.132 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".