A framework for the systematic consideration of ethics, equity, feasibility, and acceptability in vaccine program recommendations
Bibliographic record
Abstract
For the successful implementation of population-level recommendations, it is critical to consider the full spectrum of public health science, including clinical and programmatic factors. Current frameworks may identify various factors that should be examined when making evidence-informed vaccine-related recommendations. However, while most immunization guidelines systematically assess clinical factors, such as efficacy and safety of vaccines, there is no published framework outlining how to systematically assess programmatic factors, such as the ethics, equity, feasibility, and acceptability of recommendations. We have addressed this gap with the development of the EEFA (Ethics, Equity Feasibility, Acceptability) Framework, supported by evidence-informed tools, including Ethics Integrated Filters, Equity Matrix, Feasibility Matrix, and an Acceptability Matrix. The Framework and tools are based on five years of environmental scans, systematic reviews and surveys, and refined by expert and stakeholder consultations and feedback. For each programmatic factor, the EEFA Framework summarizes the minimum threshold for consideration and when further in-depth analysis may be required, which aspects of the factor should be considered, how to assess the factor using the supporting evidence-informed tools, and who should be consulted to complete the assessment. Research, particularly in the fields of vaccine acceptability and equity, has validated the utility and comprehensiveness of the tools. The Framework has been successfully used in Canada for clear, timely, transparent vaccine guidance with positive stakeholder feedback on its comprehensiveness, relevance and appropriateness. Applying the EEFA Framework allows for the systematic consideration of the spectrum of public health science without a delay in recommendations, complementing existing decision-making frameworks. This Framework will therefore be useful for advisory groups worldwide to integrate critical factors that could impact the successful and timely implementation of comprehensive, transparent recommendations, and will further the global objective of developing practical and evidence-informed immunization policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.646 | 0.597 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.027 | 0.014 |
| Science and technology studies | 0.012 | 0.044 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".