Optimizing communication material to address vaccine hesitancy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Vaccine hesitancy (the reluctance to accept recommended vaccines) is a complex issue that poses risk communication challenges for public health authorities and clinicians. Studies have shown that providing too much evidence on vaccine safety and efficacy to those who are vaccine-hesitant has done little to stem the growth of hesitancy-related beliefs and fears. The objective of this paper is to describe good practices in developing communication materials to address vaccine hesitancy. An inventory of vaccination communication materials in Canada was assessed according to the Council of Canadian Academies Expert Panel on Health Product Risk Communication Evaluation (2015). Many of the current communication products could be improved to better align with evidence-based risk communication best practices. Five best practices were identified. First, identify target audience and establish trust. Second, provide both the risks and benefits of vaccination, as most people are looking for balanced information. Third, give the facts before addressing the myths. Fourth, use visual aids. Fifth, test communication material prior to launch. Applying these best practices to current or future communication products will help vaccine providers (including physicians, nurse practitioners, pharmacists, public health professionals) to develop communication materials that are sensitive to the complex ways that people process and value information and thus more likely to optimize vaccine uptake in their communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it