COVID-19 vaccine policy development in a sample of 44 countries – Key findings from a December 2021 survey of National Immunization Technical Advisory Groups (NITAGs)
Bibliographic record
Abstract
National Immunization Technical Advisory Committees (NITAGs) are tasked with the responsibility of guiding ministries of health and national immunization programmes in their policy development processes. Many NITAGs rely on evidence reviewed by the World Health Organization's (WHO) Strategic Group of Experts(SAGE) on immunization and aim to adapt WHO's recommendations to their respective contexts. This relationship took on exceptional importance since the onset of the COVID-19 pandemic, during which NITAGs have expressed a notable struggle to craft appropriate policies on population prioritization and vaccine utilization in the face of supply constraints and complex programmatic and delivery logistics. This online survey was conducted to assess the usefulness of the SAGE guidance documents for COVID-19 vaccine policies and to examine the persisting needs and challenges facing NITAGs. Results confirmed that SAGE recommendations concerning COVID-19 vaccines are easy to access, understand, and adapt. They have been found to be comprehensive and timely under the data and time constrained circumstances confronting SAGE. The Global NITAG Network (GNN) appears to be the most popular vehicle for addressing questions among high income countries, in contrast to lower income countries who favour WHO Country or Regional Offices. NITAGs place much value on interaction with other NITAGs, which requires facilitation and could benefit from increased opportunities, especially within regions. It is further noted that some NITAGs have had to tackle issues during the pandemic not typically considered by SAGE, such as supply chain logistics and vaccine demand. Learning from the COVID-19 experience offers opportunities to strengthen NITAGs and the pandemic recovery effort through the development of more concrete procedures and consideration of more varied types of data, including implementation effectiveness and uptake data. There is also an opportunity for an increasing involvement of Country Office WHO personnel to support NITAGs, while ensuring information and evidence needs of countries are adequately reflected in SAGE deliberations.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".