Developing and Maintaining Public Trust During and Post-COVID-19: Can We Apply a Model Developed for Responding to Food Scares?
Bibliographic record
Abstract
Trust in public health officials and the information they provide is essential for the public uptake of preventative strategies to reduce the transmission of COVID-19. This paper discusses how a model for developing and maintaining trust in public health officials during food safety incidents and scandals might be applied to pandemic management. The model identifies ten strategies to be considered, including: transparency; development of protocols and procedures; credibility; proactivity; putting the public first; collaborating with stakeholders; consistency; education of stakeholders and the public; building your reputation; and keeping your promises. While pandemic management differs insofar as the responsibility lies with the public rather than identifiable regulatory bodies, and governments must weigh competing risks in creating policy, we conclude that many of the strategies identified in our trust model can be successfully applied to the maintenance of trust in public health officials prior to, during, and after pandemics.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".