Measuring vaccine hesitancy: The development of a survey tool
Bibliographic record
Abstract
In March 2012, the SAGE Working Group on Vaccine Hesitancy was convened to define the term "vaccine hesitancy", as well as to map the determinants of vaccine hesitancy and develop tools to measure and address the nature and scale of hesitancy in settings where it is becoming more evident. The definition of vaccine hesitancy and a matrix of determinants guided the development of a survey tool to assess the nature and scale of hesitancy issues. Additionally, vaccine hesitancy questions were piloted in the annual WHO-UNICEF joint reporting form, completed by National Immunization Managers globally. The objective of characterizing the nature and scale of vaccine hesitancy issues is to better inform the development of appropriate strategies and policies to address the concerns expressed, and to sustain confidence in vaccination. The Working Group developed a matrix of the determinants of vaccine hesitancy informed by a systematic review of peer reviewed and grey literature, and by the expertise of the working group. The matrix mapped the key factors influencing the decision to accept, delay or reject some or all vaccines under three categories: contextual, individual and group, and vaccine-specific. These categories framed the menu of survey questions presented in this paper to help diagnose and address vaccine hesitancy.
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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 | 0.064 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".