A framework for research on vaccine effectiveness
Bibliographic record
Abstract
The need for a systematic approach to research on vaccine effectiveness (VE) is increasing with growing numbers of vaccines and complexity of immunization programs. The diverse scientific fields that investigate how vaccines work and why they fail continue to evolve, yet definitions related to such advances have not kept pace. Researchers in disciplines ranging from basic science through immunopathology, clinical and epidemiological research, and mathematical modelling need more precise definitions to promote communication and interdisciplinary VE research to ensure that studies are designed to appropriately address relevant questions. To meet these aims, we suggest standardized definitions, consider models of vaccine failure, and offer general approaches for incorporation into study design. We further propose a framework for conducting VE research that builds on the traditional epidemiological triad of host, pathogen and environment, and also includes additional elements such as characteristics of both the vaccine and vaccinee, the effect of time on likelihood of exposure and protection, and the impact of environment and pathogen, as well as how outcomes of interest and study design may impact observed vaccine effectiveness. The framework is relevant to researchers in all disciplines who investigate the effectiveness of vaccines and vaccination programs and why they may fail. Stronger research in this field will help policy makers optimise decision-making on vaccination programs, ensuring we maximize the health benefits of vaccines. It is also important for clinicians communicating the benefits of vaccines to the public.
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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 | 0.070 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".