Improving the Suitability of Vaccine Design for Immunisation Programmes and Enhancing Vaccine Policy Quality Through User Research
Bibliographic record
Abstract
BACKGROUND: The achievement of the goals of the Immunization Agenda 2030 (IA2030) requires vaccines to be developed and implemented that meet the needs and requirements of the final users: vaccinators and vaccinees. A detailed and shared understanding of these needs should inform policy and programme guidance directing stakeholders' efforts and investments. Currently, relevant guidance documents only partially capture vaccine users' perspectives. METHOD: To help overcome this gap, we propose an operational research method grounded in the principles of the design approach that systematically maps and integrates user perspectives in vaccine development, policy, and implementation decisions. RESULTS: The method, named the seven Ws, guides researchers through a three-step process. First, it clarifies the contribution of a vaccine to solving a public health problem-the solution-problem fit. Second, it maps potential implementation strategies for the vaccine in different settings. Lastly, it describes the relevant vaccine's use cases across the implementation strategies, elucidating the user requirements for the vaccine to be successfully implemented-the solution-provider and solution-user fits. CONCLUSIONS: By explicitly pursuing these three fits, policymakers, vaccine developers, and programme managers will be able to better contribute towards the achievement of the IA2030 goals. This framework is intended as a conceptual contribution rather than an empirical validation study.
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How this classification was reachedexpand
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".