Speaker 1: Hello, and welcome to this video on risk. We're going to have a look at what risk is and take a look at how to use a risk matrix. First of all, what is risk? Now, if you think about it, life is full of risks, from the time we wake up to the time we go to sleep, and even in between. For example, there are risks associated with breathing. Did you know that air pollution causes about 7 million deaths each year? There are risks associated with eating. Foodborne illness causes about 420,000 deaths each year. And then there's traveling. Road accidents cause over 3,000 deaths each day. In fact, every day we have to make constant decisions about the risks we face. Now, there are situations in which we need to know what the risk is from certain events so that appropriate action can be taken. For example, what's the risk to health from a new influenza virus, an industrial chemical accident, or a bushfire? But first, what exactly is risk? Risk can mean different things to different people and in different contexts. But in very simple terms, risk can be thought of as the product of how likely someone is to be exposed to something bad, a hazard, and the consequence, which is how bad the outcome will be. Let's have a look at an example. Say you have to cross a road and you're trying to figure out what the risk associated with this is. Let's take a look at some different scenarios. In the first scenario, our hazard is a bicycle. Let's assume there are only bicycles allowed on this road. Let's also assume that the road is not that busy and there's only one bicycle every five minutes on that road. Now, because bicycles don't go on that road very often, the likelihood of the bicycle crashing into you is low, right? If the bicycle does crash into you, the consequence of that would be relatively low, wouldn't it? So in this scenario, the likelihood and the consequence are both low, therefore the overall risk is low. Now obviously, if there are bicycles travelling more frequently, the likelihood of a crash would be higher, but the consequence, if a bicycle did crash into you, would still be low. But what if things were different? Let's change that bicycle to a truck. Now with a truck passing every five minutes, the likelihood of a crash would still be low. However, the consequence is far more severe. Now imagine if it was a busy truck route, and trucks were going on this road very frequently. In this scenario, both the likelihood of a crash and the consequence will be high, which means that the overall risk would be high. You wouldn't even consider crossing this road, right? A simple way of visualising this and figuring out the overall level of risk is by using a risk matrix. In a risk matrix, information about consequence and likelihood can be used to determine the overall level of risk. Let's have a look at a simple risk matrix. A risk matrix is basically a table or grid with a measure of likelihood on one side and consequence on the other, graded from low to high. The combination of the two, which is the overall level of risk, can be categorised as low, medium or high, and colour-coded. So let's get back to our example. In the scenario in which there are bikes travelling very infrequently, the likelihood is low and the consequence is low, therefore the overall risk is low. When there are more bikes, the likelihood is high but the consequence is still low, and therefore the overall risk is medium. Let's consider the truck now. In the scenario where trucks are infrequent, the likelihood of a crash is low but the consequence is high, the overall risk is medium. And if it's a busy truck route, the likelihood and consequence are both high, and therefore the overall risk is high. So that's how a risk matrix can be used to determine the overall level of risk using an assessment of likelihood and consequence. There are many different types of matrices in use with varying degrees of complexity. They can have many categories and different grades of risk. They may also have different ways of defining categories of likelihood and consequence. For example, a high likelihood may mean that it's expected to occur in most circumstances, and a low likelihood may mean that it may occur only very rarely, such as once in 100 years. With consequence, a high consequence may mean multiple deaths, and a low consequence may mean only minor injuries. So the risk matrix and the categories that are used can vary and therefore should be relevant to the situation and context in which the risk assessment is done. Regardless of this, risk matrices follow the same principle. Although risk matrices have their limitations, such as being subjective, not being consistently interpreted, and not taking time frames into consideration, they are a useful and practical tool to categorize risk. And that's an introduction to risk and how to use and interpret a risk matrix.