That was the start of my interest in interrogating workforces and examining their potential optimization. What interests me is the relationship between work, workforces and safety or outcomes. My particular focus is in modelling the demand for labour, particularly highly skilled labour, where work is complex.

The best endorsement I’ve ever received is that I “am not afraid to be unpopular”.I think this relates to my research, which tends to challenge the new and shiny ‘solutions’ that the health-care-policy world seems to favour. Health care has its own culture, which makes original evidence-based thinking a challenge to introduce or implement.

How do you model a workforce?

You collect data to build an accurate picture of the workforce, how it is structured and how it behaves. You also look at labour expenditure, as well as work done and work that is left undone, and look for points of improvement.

The basic steps are, first, understand the work and its purpose. Next, break down the work into component parts and count them. You can then start collecting data from which to build a model to explain how the workforce currently works; it will only ever be a representation of the real world. I spend a good proportion of my time understanding the work and the context in which it’s carried out, and checking any assumptions against that reality.

Finally, you can use the data to re-model the workforce to show how it might work better if adjustments were made, for example by altering workflows or the number of staff with particular qualifications. This can also reveal the risk that some work will not be done, and the consequences of that.

For example, my modelling for the inflammatory-bowel-disease specialist-nursing workforce recommended a staff-to-patient ratio of 2.5 full-time-equivalent specialist nurses per 250,000 patients. It has become the European standard.

No model is perfect, some are more useful than others, and we initially aim for roughly right, rather than precisely wrong. The models are iterative, too, so with more data over time, they refine and generate greater insights.

How can we level the professional playing field and create more opportunities for women in academia?

The structural inequality in society is reflected in scientific and academic careers. Whether it’s nursing, academia or clinical sciences, you see the scissor effect — where there are plenty of women in the lower ranks but their representation slides in higher ranks.

We lose women in science, engineering and technology (the ‘leaky pipeline’) for a number of reasons. Yes, women are more likely to take up caring responsibilities, but there are other factors, such as the authority gap — the lack of visible female leaders in the field. Plus, there’s an opportunity gap: it’s harder to access those opportunities when you are working part-time or have other responsibilities.

A lack of confidence can also be an obstacle. And factors such as ethnicity and social class have an impact: you might not have the same opportunities to network as those who hold more privilege or structural advantage.

How has that applied to your own career?

Creating equity of opportunity is really important, given that some people do not have access to the same networks and finances as others.

I grew up in Lewisham, southeast London, where I was lucky to have a great mathematics teacher, Mrs Ferguson, who spotted that I had an affinity for numbers. At the time, very few people went to university. I don’t think I knew anyone who did. Both my parents left school at 14, which was usual at the time, but they were very supportive of me, and I became the first person in my family to go to university.

Being from a working-class background has been an advantage and a disadvantage. Following a different path has probably built my personal resilience, because it has meant facing different kinds of problem and having to challenge the assumptions of others. I’ve learnt that my accent does not match my CV, for example!

Quite a lot of things can be done to tackle inequity of opportunity. There are very practical things, such as removing people’s names from CVs during the recruitment process to help mitigate bias, but that doesn’t address the fact that some people might not feel confident enough to apply for those jobs in the first place.

Workplace leadership and culture are important: employers need to ask, ‘Whose work are we valuing?’ and ‘Who is at our decision-making table?’ A lot of commercial organizations are one step ahead, embracing diversity of thought, rather than just diversity of characteristics.

What can researchers do to increase their chances of success?

Seek out mentorship and build your network. It’s not an easy thing to do, particularly if you’re from a different sort of background from all the people you’re trying to influence. But be open to opportunities, and think about the people who could be allies. They’re not always the people that you might think; it’s not always established leaders.

Also, don’t always think in terms of linear career progression. There’s a real pressure just to keep climbing the ladder, but there will be times when that might not be right for you. Sometimes, taking a sidewards step is just as helpful.

The other thing is to be allies to your colleagues: hold the ladder while someone’s trying to get up it, and don’t pull the ladder away when you’re up there. That’s a rule I’ve always tried to follow.