Drawing from the ‘bank of credibility’: perspectives of health officials and the public on media handling of the H1N1 pandemic
Bibliographic record
Abstract
The H1N1 global pandemic of 2009–10 was moderate in its severity, which led many members of the public to denounce news organizations for ‘hyping’ the threat posed by the virus. This outcome was troubling as it portended a potentially cynical public audience in the event of a future emerging infectious disease. As we face a new Public Health Emergency of International Concern (PHEIC) with COVID-19, public trust in public health information and mediated messaging is more important than ever. Health authorities aim to inform the public through various avenues, particularly by engaging news media as a bridge to deliver pertinent information. We draw on the Trust, Confidence, and Cooperation (TCC) Model to examine how citizens and health officers evaluated news coverage of the H1N1 pandemic in Canada and the impact it had on public trust in public health recommendations. Following the H1N1 pandemic, we conducted interviews (n = 28) with senior health officials in Canadian federal and provincial jurisdictions and focus groups with general population Canadians (n = 130) in three provinces. Findings showed that many health officials and members of the public considered that the pandemic H1N1 was hyped in news coverage and that the immunization campaign was portrayed as chaotic, potentially affecting trust in pandemic messaging and response activities. Our results highlight the key role of news coverage in pandemic communication. Further, we recommend that health authorities complement their media engagement with direct communication with citizens; and increased training for public health officers to engage with news media and promote public trust. The lessons of this study remain crucially relevant given that legacy news media continue to be important sources of health information as the world fights to control the COVID-19 pandemic.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.047 | 0.040 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.014 |
| 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".