Descriptive analysis of immunization policy decision making in the Americas
Bibliographic record
Abstract
OBJECTIVES: Reducing and eliminating vaccine-preventable diseases requires evidence-based and informed policy decision making. Critical to determining the functionality of the decision-making process for introduction of a new vaccine is understanding the role of the national immunization technical advisory group (ITAG) in each country. The aim of this study is to document the current situation of national level immunization policy decision making for use in the Pan American Health Organization (PAHO) ProVac Initiative. METHODS: A structured 66-variable questionnaire developed by the World Health Organization (WHO) in collaboration with the University of Ottawa was distributed to all WHO regions; it was composed of dichotomous, multiple-choice, and open-ended questions. Questionnaires were e-mailed or faxed to the six WHO regional offices and the offices distributed them to all member states. This paper analyzes surveys from the Americas as part of PAHO's ProVac Initiative. RESULTS: Twenty-nine countries of the Americas answered the survey. They conveyed that immunization policy making needed to be improved and further supported by organizations such as PAHO. Areas of improvement ranged from organization and technical support to strengthening capacity and infrastructure to improved coordination among stakeholders. This survey also highlighted a variety of ITAG processes that need further investigation. CONCLUSION: This survey supports the efforts of PAHO's ProVac Initiative to disseminate knowledge and best practices for an immunization policy decision-making framework through the development of clear definitions and guidelines. By highlighting each problem noted in this study, ProVac will assist countries in Latin America and the Caribbean to build national capacity for making evidence-based decisions about introduction of new 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".