Driving the life course approach to vaccination through the lens of key global agendas
Bibliographic record
Abstract
Globally, our population is ageing at an unprecedented rate and by 2030, which marks the end of the United Nations (UN) Decade of Healthy Ageing, the number of people aged 60 years and older will be 34% higher than today, reaching 1.4 billion. Vaccination is one of the most effective public health interventions of modern times and a key action in fostering healthy ageing throughout the life-course. To promote wellbeing at all ages, global agendas including the WHO Immunization Agenda 2030, the UN Decade of Healthy Ageing and the World Health Organization (WHO) Global Report on Ageism outline strategic actions and guidance to help implement policies and programs. Yet, the linkages between healthy ageing, functional ability and adult vaccination are not substantively recognized or integrated as cross-cutting themes, which impacts operationalization into national immunization plans. When aligned and connected strategically, these agendas have potential to substantially contribute to policy change to prioritize life-course immunization and support the preservation of function at all stages of life. This article describes the intersecting goals and visions of these strategic agendas and identifies specific elements of overlap, which when connected, could strengthen the development of comprehensive and effective national immunization policies.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".