Best practices in public risk communication during enteric illness outbreak investigations: a scoping review
Bibliographic record
Abstract
Introduction Public risk communication is intended to inform and protect the health of individuals during enteric illness outbreaks. However, there is limited practical research that assesses the effectiveness of communication during outbreaks. The aim of this study was to identify best practices in public risk communication during enteric illness outbreak investigations. Methods A scoping review of five bibliographic databases and gray literature was conducted to identify studies that described public communication during foodborne, waterborne, or enteric zoonotic outbreaks. Eligibility criteria were applied to citations and then full text by two independent reviewers. Data from included studies was extracted and synthesized into categories. Evidence adequacy and agreement were assessed and used to assign an overall level of confidence for each best practice. Results In total, 25 studies were included with most studies occurring in North America and Western Europe. Seven principles, nine practices, and eight platforms were identified. Of these, six principles, four practices, and two platforms received a high confidence rating in their overall effectiveness. Discussion Effective risk communication during enteric illness outbreak investigations requires public health authorities to identify, characterize, tailor information to, and meaningfully engage with their target audiences, build relationships and collaborate with media outlets, and maintain and increase credibility to deliver trustworthy risk communication messages.
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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 | 0.037 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".