‘Damned if you do, and damned if you don’t’: communicating about uncertainty and evolving science during the H1N1 influenza pandemic
Bibliographic record
Abstract
During the 2003 SARS outbreak in Toronto, Canada, communication with the public was poorly executed by health authorities. Key problems included mixed and unclear messages, widespread public confusion, and attributions of incompetence toward health officials. Subsequently, Canadian health officials developed pandemic plans that included specific sections dedicated to communication. Plans counseled a strategy of transparent risk messaging to give people the information they need and build public trust. When the H1N1 influenza pandemic arrived in Canada in 2009, these plans were put to their first test in a major public health event. However, many of the same problems that existed during SARS arose again during pH1N1. This study investigates the dissonances between the ideals and reality of communication during pH1N1 based on analyses of two data sources: (1) key informant interviews with senior health officials (n = 28) from federal and three provincial (Alberta, Manitoba, Ontario) health jurisdictions in Canada; and (2) focus groups (n = 15) with general population Canadians (n = 140) in Alberta, Manitoba, and Ontario. Discussions with participants showed that even with a transparent communication approach, aspects of the pandemic, such as its ‘risk’ and the complexities of the immunization campaign, proved difficult to convey without causing public confusion. Members of the public often resorted to their own inventories of knowledge – usually those related to seasonal influenza – to interpret and make sense of pandemic messaging, but these did not guarantee accurate understandings. The inherent uncertainty of a real-time pandemic was also a difficult concept to communicate to a public with little prior experience of such an event. While transparent communication was intended to build trust, resulting confusion fueled a loss of confidence in health officials. A more ‘reasoned’ approach to transparency needs to inform future pandemic communication and further research is required to determine how to refine such an approach.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.034 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".