Especially in the beginning, scientists, governments and individuals were uncertain how best to respond to the Covid-19 pandemic. While a certain level of disagreement and contestation is to be expected during a pandemic, members of the public tend to question the legitimacy of “expert advice” when dealing with new issues. One reason is that there is not always a “direct connection between an evidence base and an optimal public health communication strategy”¹.
Uncertainty is “salient in a pandemic, where we often do not know enough about the effectiveness of treatments and policies to be confident of their outcome” (Batteux et. al 2021)24. Throughout the pandemic, there was great suspicion levelled towards vaccines against Covid-19² due to the public’s uncertainty in relation to their development, and safety. These uncertainties³ around facts, numbers and science due to limited knowledge or ignorance are examples of epistemic uncertainty⁴, which are particularly challenging when communicating with the public.
The contestation around facts and the idea of a “post-truth” society makes it even more challenging for scientists to communicate their own uncertainties⁵.
People are looking for assurance in uncertain times, particularly during a pandemic. Understandably, they wanted to find out what to do to protect themselves and are eager to believe that there is a cure for Covid-19. Rumours to use steaming⁶, tea⁷ or siam weed⁸ were widely shared, especially on social media, but all false.
Unfortunately, bad actors in the space have realised that people often seek certainty regardless of whether the information is true or not. And “[false information] never fails to provide proof of its claims, even if that proof is wholly fabricated”, Bailey 2020⁹.
In addition, our biases can lead to more uncertainty, e.g. the publication bias¹⁰. This is a bias towards research that publishes positive findings and consequently studies that criticise existing theories or ideas are less likely to be published. As great as positive findings are, this is not always possible in the world of research. In instances when researchers are not able to publish positive results, bad actors might prey on this and publish unfounded claims aimed at what the public wants to hear.
There are several challenges in relation to communicating in times of uncertainty.
People perceive ambiguous situations as undesirable and want to avoid them, a challenge called ambiguity intolerance¹¹. Uncertainty can result in avoidance or a higher level of discomfort.
In addition, people tend to prefer known risks rather than the unknown risks and focus their attention on already existing information, a process called ambiguity aversion¹². This then makes communicating uncertainties complicated because people are likely to be closed off in the face of ambiguity.
It can have negative implications if experts disagree or are uncertain about a specific topic themselves, even though this is to be expected. In specialised fields, like example medicine, communicating uncertainties can have detrimental effects on people’s trust and satisfaction¹³.
Communicating uncertainty to the public can also help to increase trust if done in the right way.
Secondly, uncertainty is inevitable and needs to be managed and normalised, not hidden. Communicating unwarranted certainty or withholding information can motivate people to look for information elsewhere, fostering a belief in rumours, misinformation, and conspiracy theories and undermine trust¹⁶.
Thirdly, consistency in communication is essential¹⁷, especially as information is changing and different approaches are required. One way to achieve this is to provide the latest information and updates about a process and the approach experts use to gather more information.
- What do you have uncertainty about?
- Where is the uncertainty?
- How will you present these uncertainties?
Here are practical ways to communicate uncertainty to help members of the public get better at processing information in an uncertain world.
Never gloss over²⁰ variance and evidence gaps and be specific about whether it is due to the incomplete understanding of a process, unreliability of measurements, insufficient data, or other sources.
Indicate uncertainty in existing data²¹ using numerical ranges in brackets, after the main value. When working with numbers, simply using words like “estimated” and “around” once is not enough to show readers that there is a level of uncertainty in the data.
In the case of future predictions²², use verbal expressions to indicate the general direction of travel, but supplement these with numerical probability ranges, and wherever possible access to underlying data.
Take care when using large numbers²³ and explain jargon if it is necessary at all. When using large numbers, remember that the difference between 1 million and 1 billion is clearer if the latter is expressed as 1,000 million.
No matter how unsettling it may be²⁴, it is better to be clear and transparent about uncertainty and ambiguity, as the discomfort of communicating these uncertainties is not worth the potential risks of failing to do so.
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