Risk know-how Framework

Risk know-how is about informed decision-making. Every risk entails a trade-off with the benefits and costs of action or inaction, and we weigh these up to make a decision.

1. Risk know-how in a community means we can:

a. Ask questions about specific risks*

*In this framework we discuss the topics as they occur in public discussions where risk is a term used indistinctly for risks and hazards. 

b. Find suitable and reliable risk information

My job is to help people find the information sources they need. But I also want to encourage information literacy – to interrogate some of their assumptions about what is reliable. As we look at search results together, I might point out different points of view, agendas and rhetorical strategies. We might start with sources from local, state or regional governments, then go to national governments and international agencies. After that, we might explore the grey literature, say from think tanks and NGOs. I also recommend tertiary information (handbooks, encyclopedias), which is great for non-specialists, and we look at the sources they use. We can suggest statistics databases that provide and aggregate raw data, which can be easier to consume than some of the disparate local data.
Chandler Christoffel
User Experience Librarian
USA

c. Understand how the framing of the information can be manipulated

My job is to help people find the information sources they need. But I also want to encourage information literacy – to interrogate some of their assumptions about what is reliable. As we look at search results together, I might point out different points of view, agendas and rhetorical strategies. We might start with sources from local, state or regional governments, then go to national governments and international agencies. After that, we might explore the grey literature, say from think tanks and NGOs. I also recommend tertiary information (handbooks, encyclopedias), which is great for non-specialists, and we look at the sources they use. We can suggest statistics databases that provide and aggregate raw data, which can be easier to consume than some of the disparate local data.
Chandler Christoffel
User Experience Librarian
USA

d. Examine a claim about the size and importance of a risk or the value of a safety measure
e. Not be surprised by the consequences of a risk taken
f. Make reasonable comparisons between potential risks and between the costs of action and inaction – trade offs
g. Appreciate the likelihood of motivated reasoning and being more likely to seek information that confirms what we prefer
h. Be aware of how new information might require a decision change about the risks that are tolerated

As an outdoor educator, we make decisions about when we need to seek shelter, because of a thunderstorm or the cold, for example. Sometimes you face the same conditions but make different decisions, because it matters how young the group is, how large a group you oversee, and how quickly you can get to shelter. What feels like the right decision one day might feel too risky a different day.
Sarah Whitaker
Forest kindergarten director
USA

i. Be respectful of the fact that other people’s risk and benefit trade-offs are not the same and that not everyone has the opportunity to act upon risks

The fishermen in our community in Bangladesh will go out sea fishing even with a cyclone or a storm approaching, because the risk of not catching fish and not being able to eat feels more present.
Sazedul Hoque
Researcher in food safety and fisheries technology
Bangladesh

2. To do this we need to:

(a) Clarify what is actually being discussed 

i. Definitions
ii. Numerator
iii. Denominator
iv. Population
v. Event and consequence
vi. Period of time

When making decisions about priorities in rural Australia, we need to clarify what the risks are to any given individual, but also how many people could be affected, how likely it is they will be affected, the kind of threat (microbial or chemical), the doses and for what period of time.
Jordan Phasey
Water treatment professional Australia

(b) Make sense of what is being said

i. Give meaning to very big and very small numbers

It is hard to give meaning to very big or very small numbers. For example, it is estimated that in 2018 about 47 million children under one worldwide were vaccinated for rotavirus. Is this a lot or a little? In fact, it represented just 35% of children under one.  

Context clarifies the number, notably the ‘multipliers’, such as population size and the accumulation of risk over time.

2(a)(v1). When making decisions about priorities in rural Australia, we need to clarify what the risks are to any given individual, but also how many people could be affected, how likely it is they will be affected, the kind of threat (microbial or chemical), the doses and for what period of time.
Jordan Phasey
Water treatment professional Australia

ii. Quantify values such as high and low risk

“High risk” or “low risk” are value judgements, with a different meaning to different people and in different contexts. Quantifying is necessary and allows people to discuss trade-offs with benefits or other risks.
For example, being hit by lightning is sometimes described as a low risk – but what does this mean? In the US, the national weather service estimates the probability of in a given year at 1 in 1,222,000 and the odds over an 80- year lifetime as 1 in 15,300. (https://www.weather.gov/safety/lightning-odds)

 

2(b)(ii). We label all pregnancies ‘high risk’ and ‘low risk’. When women are screened for foetal abnormalities, results are most often reported as a statistical chance and sometimes the terms “increased chance” or “low chance” are used. All hospitals have a number they use as a cut-off between those results that are “increased chance” (further testing is offered) and “low chance” (no further testing). This number is usually 1 in 150. This means that all women who have a result between 1 in 2 and 1 in 150 will be offered a diagnostic test such as CVS or amniocentesis.
Sarah-Jayne Ambler
Research midwife
UK

iii. Understand the effects of comparisons and context

The actual number of violent crimes might not have changed in a few years, but if the number of other types of crime have gone down then violent crimes as a proportion of the total numbers of crimes will increase.

iv. Differentiate between absolute and relative risk – and give them meaning with natural frequencies

The number of plane crashes has doubled (relative risk) does not tell us how many planes have crashed compared to the total number of flights (absolute). Instead of talking of a risk increasing by 20% it can be more helpful to see it as 1 more person in 100 will get the disease, for example.

 

2(b)(iv). The people who contacted our information service were worried about the AZ vaccine causing blood clots, so we wanted to offer some context for the numbers. They needed some meaning for the percentages – how many in each group would be affected. We used an infographic from the Winton Centre showing the risks to different ages and kept the information in the context of other side effect risks and risks from COVID-19 itself – helping to visualise the trade-offs.
Rocio Perez Benavente
Maldita Ciencia
Spain

v. Understand recurrence intervals and averages

A 1-in-100-year flood is an average over the data we have, so two could happen in back-to-back years, or 160 years apart. There is more than one type of average, so it is always important to know which one it is and how it is affected by the range it covers.

vi. Understand single event probabilities

Probabilities such as there is a 30% chance of rain or a 50% chance of side effects from a drug need a reference class. People interpret these differently (such as, it will rain in 30% of an area or for 30% of the day). We need to know what time and space it refers to.

vii. Know uncertainty

Few things are 100% certain – and everything less than this is uncertain. So how confident can we be? Where uncertainty is known, it should be clear if this is incorporated in risk predictions. Often uncertainty cannot be quantified, but decisions still need to be taken. As more information comes to light and we have a clearer idea of the extent of what we don’t know, we may have to adjust how we compare risks or compare risks with benefits and change our decision. Talking about this is helpful for communities navigating risks.

 

2(b)(vii). Humans can have an ambivalent relationship with uncertainty. An entirely predictable life without risk would be unimaginably dull, and you can see that very clearly with children. In the workshops I run even just talking about uncertainty explicitly and expressing their fears can unburden people.
Tim Gill
Global advocate for children’s play and mobility
UK

viii. Discuss concepts that affect the accuracy of risk information

Few things are 100% certain – and everything less than this is uncertain. So how confident can we be? Where uncertainty is known, it should be clear if this is incorporated in risk predictions. Often uncertainty cannot be quantified, but decisions still need to be taken. As more information comes to light and we have a clearer idea of the extent of what we don’t know, we may have to adjust how we compare risks or compare risks with benefits and change our decision. Talking about this is helpful for communities navigating risks.

i. Conditional probabilities
The likelihood of a particular outcome given its relationship to something else.
This may include understanding false alarms and false reassurance. If we are looking at measures (such as a test) we need to know the probabilities of them accurately detecting the answer. When we have the result, we need to assess how likely it is to give us an accurate picture or prediction.

ii. Correlation/causation
When an association (or correlation) is observed between two things, it does not necessarily mean that one causes the other. If we see an association, such as a change in river management and a change in fish stocks, it prompts more questions such as whether other factors could be influencing fish, how confident we are in the data and whether more should be collected.

iii. Adjustment
How data imbalances have been corrected, or not, before summary risk statistics are calculated.

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