Understanding Trust in Public Health Communication During Crises: The Role of Information, Spokespersons, and Channels
Bibliographic record
Abstract
The COVID-19 pandemic has demonstrated the crucial role of crisis communication in promoting the adoption of risk protective measures, combatting mis/disinformation, and maintaining trust in officials. In a situation of high uncertainty, rapidly evolving conditions, and an excess of mis/disinformation, the COVID-19 pandemic emphasized the need for reliable and effective information from officials. \nFour interrelated studies were used to explore critical success factors associated with maintaining trust in crisis communications during a pandemic. First, a qualitative systematic review was conducted with 13 studies, resulting in 10 descriptive themes related to maintaining trust during emerging infectious disease. Next, a mixed methods study included: a content analysis of Facebook posts for guiding principles for crisis communication; a sentiment analysis of comments to determine the emotional response; and chi square tests to determine significant differences across sources, guiding principles, and sentiments. Third, a mixed methods study of 33 Canadian influencer crisis messages on Instagram was conducted to: describe the use of behaviour change theory constructs; an engagement analysis; a sentiment analysis; and chi square tests to determine significant differences across variables. Finally, semi-structured interviews were conducted with 12 Canadian adults who were not fully vaccinated against COVID-19 and were thematically analyzed, describing four interrelated themes related to crisis communication and trust. \nThe findings of the research demonstrate how guiding principles for crisis communication that demonstrate trustworthiness and constructs from behaviour change models are not being widely or consistently used in COVID-19 crisis messages. Furthermore, the public’s response to crisis messages on social media is neutral at best but often shows negative emotional response to messaging and low overall engagement with official posts. Interviews with vaccine hesitant individuals also show that the perceived low use of guiding principles is negatively impacting trust and contributing to vaccine hesitancy. Results highlight the need and opportunity for crisis communication to be audience-centred and co-created so that messages reflect the needs and values of various communities, in addition to being evidence-based and rooted in guiding principles and theory to maintain trust.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".