Optimizing communication material to address vaccine hesitancy
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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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 | 0.032 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".