A decade of adaptation: Regulatory contributions of the World Health Organization to the Global Action Plan for Influenza Vaccines (2006–2016)
Bibliographic record
Abstract
The Global Action Plan (GAP) for Influenza Vaccines is a decade-long initiative that brings together a diverse range of stakeholders to work towards reducing anticipated global shortage of influenza vaccines and ensuring more equitable access to vaccines during the next influenza pandemic. Since its inception in 2006, significant progress has been made towards all the main objectives of GAP, namely: (1) an increase in seasonal vaccine use, (2) an increase in vaccine production, and (3) progress in research and development of more effective vaccines. The Technology Transfer Initiative (TTI), conceived and managed by WHO under the GAP, contributed to increasing regional influenza vaccine production capacity. This was achieved by facilitating technology transfer in 14 low- and middle-income countries, through grants to manufacturers to establish or strengthen influenza vaccine production capacity and support to their national regulatory authorities. Five of the countries subsequently licensed locally produced influenza vaccines; two pandemic and three seasonal vaccines received WHO prequalification. The success of GAP can be largely attributed to the regulatory support provided by WHO to both manufacturers and regulators. This support had two components: (1) direct regulatory support to GAP/TTI, and (2) support to GAP-related WHO programmes, such as the Pandemic Influenza Vaccine Deployment Initiative in 2010 and the Pandemic Influenza Preparedness Framework since 2013, especially in non-vaccine-producing countries. Temporary adaptation of the assessment process for influenza vaccines in the WHO Vaccine Prequalification Programme to the A(H1N1) pandemic situation in 2009 was instrumental to the success of the WHO Pandemic Influenza Vaccine Deployment Initiative in its attempt to meet the demand for pandemic vaccines in countries that received donated vaccines.
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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 | 0.057 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".