Descriptive analysis of immunization policy decision making in the Americas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it