Consultation on options to reach gender equity in the Investigator Grant scheme: Consultation closed

PROFESSOR CAROLINE HOMER, Chair of the National Health and Medical Research Council:

[Professor Homer is speaking to the audience. Visual of PowerPoint slide saying National consultation, August 2022: Options to reach gender equity in the Investigator Grant scheme, Professor Anne Kelso AO, Chief Executive Officer is displayed. Timestamp 0.00]

NHMRC has been conducting a national consultation to seek feedback from the sector on options to reach gender equity in the Investigator Grants scheme. Since 1 August, the CEO, Anne Kelso, and the team have had forums in Brisbane, Townsville, Sydney, Melbourne, Adelaide, Perth and this morning in Canberra. Today is the final open forum and today is the final day of the online consultation period, so I’m really encouraging you if you haven’t had a chance to put your comments online, please do so today, but we’re also really happy to take comments and questions today.

So the way the session works is that the CEO will go through a series of slides and present the issues and the options and then we will open for questions and we’ll explain to you how we’re going to do that along the way using a process called Menti, which is an online system.

So I’d like to introduce the CEO of NHMRC, Professor Anne Kelso, and many of you will know Anne, but just a brief bio to situate her expertise.

Anne did a PhD in immunology in Melbourne, at the University of Melbourne, spent many years as a biomedical researcher, a post Doc at the Swiss institute for experimental cancer, she came back to WEHI and then went to the Queensland Institute of Medical Research.

While in Brisbane, Anne also held the role of director for the CRC [Cooperative Research Centre] for vaccine technology and in 2007 came back to Melbourne as director of the WHO [World Health Organization] collaborating centre for influenza and continued her own research as part of an NHMRC Program Grant.

Anne took up the CEO role of NHMRC in 2015 and I think it's been an incredible time for NHMRC since Anne's leadership over that period.  She's overseen a major review and reform of NHMRC grant program and you all know about that because you're here today to understand more about the Investigator Grants.

Anne has made a huge contribution I think particularly over this period around a fierce and enduring commitment to gender equity and we're really grateful that she's taken the lead on this across the sector and across the country and I know that you will find this presentation really interesting and we're really, really committed to hearing your questions and comments.

So, Anne, handing over to you now, thank you.

PROFESSOR ANNE KELSO AO, Chief Executive Officer of the National Health and Medical Research Council: 

[Professor Kelso is speaking to the audience. Visual of PowerPoint slide saying National consultation, August 2022; Options to reach gender equity in the Investigator Grant scheme; Professor Anne Kelso AO, Chief Executive Officer is displayed. Timestamp 2:36]

Thank you very much, Caroline. We do appreciate having you with us as our Chair of Council and your willingness to host this session for us and to facilitate the discussion we're going to have later on.

And this is the last of our presentations in quite a series, as Caroline has outlined, and I'm just delighted that so many people are joining us online.  I know not everybody could get to the in-person ones as we toured the country and so I'm very pleased that we've been able to offer this opportunity and that so many people are taking it up today.

I'm going to start with an anecdote as told in a book by Jessica Nordell and it's about a 43-year-old neurobiologist at Stanford, Ben Barres, who began hormone treatment.  He'd been born as a Barbara and he was very keen to begin his transition.  He was worried, though, about the impact of this transition on his career and, in particular, whether students would still want to come and work in his lab and whether he'd go on getting invitations to meetings as a transgender researcher.

The scientific community did react, but not in the way that Ben had feared. After his transition, people who didn't know that Ben was transgender started listening to him more carefully. They stopped questioning Ben's authority and Ben, now middle-aged, white and male, was no longer interrupted in meetings and he even had better service shopping. At one conference a scientist who didn't know that Ben was transgender was overheard to say "Ben gave a fantastic seminar today, but of course his work is so much better than his sister's”, not realising that Barbara was him.

Once when Ben was still living as Barbara, was an undergrad at MIT, and solved a problem in a maths class, the professor said to him, to her at that stage, "your boyfriend must have solved it for you".  Barbara was offended at being accused of cheating, it was only later as Ben that he saw, as he said, how differently men and women are treated according to their gender identity.

I've found this a really compelling story and it's a true story, but it's also only one data point and I think it's very important to note that there's a lot of data to support the interpretation of Ben's experience as a researcher after his transition.

[Visual of PowerPoint presentation progresses to slide with heading – Gender differences in career circumstances – women. Timestamp 5:13]

Now, these gender differences - whoops, did that move forward, yes - gender  differences in people's career circumstances and experiences are often illustrated with this iceberg analogy and I think many of us will appreciate that, as shown in what we can see above the waterline here, that women are more likely to be the major caregivers for children and parents, they undertake most domestic duties, we hear about this all the time. They're expected to undertake more service roles at work; they're more likely to be on short-term contracts; and they're more likely to experience sexual assault and harassment.

What's not so obvious is what's shown here below the waterline, but it is what many women experience, that they're less likely to be seen as leaders; they're less likely to see themselves as leaders; they're less likely to have their ideas recognised and taken up; they're less likely to be seen as technically competent and less likely to be seen as committed to their careers. They're more likely to under-report their achievements and they're more likely to be excluded from decision making.

The thing about these things which are below the waterline is that they're not visible to people who don't experience them and so we often only know about them if we experience them ourselves or if we see them affecting the people who are very close to us and so these are the issues that collectively add up to systemic disadvantage for women as they make their way through their careers in health and medical research, as in many other professions in this country and as in many other similar countries around the world.

So, when we think about the opportunities that are available to women to pursue their research careers, then we know that having children, looking after partners and parents are important issues that generally affect women more than men, but there's a whole lot more to consider.

Women, despite their talent, despite their commitment, and despite the investment that's been made in their education and training, face higher barriers at every level of their post-doctoral careers.  Now, that's why NHMRC has a Gender Equity Strategy…

[Visual of PowerPoint presentation progresses to slide with heading – Gender Equity Strategy (2022–2025). Timestamp 7:21]

… and this is our second strategy that was released in June this year and we've articulated a vision for a gender diverse and inclusive health and medical research workforce not just because it's fair and it's the right thing to do, but because Australia needs to take the full range of talent, to draw on the full range of talent, if we're going to address the health issues that we face as a country now and in the future.

We know that gender equity strengthens the health and medical research workforce because it brings diverse points of view and it also means that the types of questions people ask and the way they view those questions depend to some extent on their gender.

Diversity in the workforce is going to lead to better health outcomes with that broader view and I hope we would all agree that everyone benefits from research environments that are safe, respectful and inclusive.

[Visual of PowerPoint presentation progresses to slide with heading – Diverse genders). Timestamp 8:15]

Now, before I go any further, it's really important to acknowledge that gender identity is diverse, gender can be non-binary and it can change over time and many non-binary people also experience systemic disadvantage in their personal and professional lives. Now, this has been a difficult issue for NHMRC to consider because we don't have data about our research workforce beyond the basic questions that we've been asking up till now. When applicants apply to us for a grant they identify themselves as male, female or other and other encompasses intersex and indeterminate and we found that the great majority of applicants have identified themselves as either female or male.

It's not a compulsory question and so some people have elected to identify themselves as other, but they've also been a larger group of people who haven't made a selection and so we don't know how to interpret that. Now, for this reason for this presentation I'm going to talk in the binary. I'm going to talk about female and male and women and men for simplicity, for the purposes of the discussion, and also to reflect that those are the data that we've collected from applicants. But this is not that we will not pay attention to non-binary applicants and we're very concerned that we consider non-binary applicants in a fair and appropriate way, so I'm going to come back briefly to that issue later and I'm very happy to talk about it further in discussion, after the presentation if people would like to do that.

[Visual of PowerPoint presentation progresses to slide with heading – Investigator Grant applications. Timestamp 9:51]

So I want to turn now to the Investigator Grant scheme itself, which of course is the scheme we're here to talk about. Most people on the call probably know that this is a career stage-based scheme.   It's also our largest scheme at NHMRC and we use 40% of the Medical Research Endowment Account that we have each year to spend on research grants on this one scheme. The scheme has five levels of seniority. They're in three categories which are run as three separate competitions within the scheme: Emerging Leadership levels 1 and 2, which are for applicants who are under 10 years post PhD corrected for career disruption, and then the more senior category, which is Leadership, within which there are three levels of seniority, L1, L2 and L3, from more junior to more senior, but run as a single competition at Leadership. The selection of the level at which to apply is up to the applicant and we provide guidance on how to choose an appropriate level for somebody's career stage and the maturity of their research career. We also ask them to justify the level that they've chosen and then we ask peer reviewers to take that into account when they're assessing the applicant's track record relative to opportunity. So, the encouragement is for applicants to apply at an appropriate level for their research level and then for peer reviewers to take that into account.

Now, what we've observed in the first three years of the scheme, 2019 through to 2021, is that the number of female applicants has declined very steeply with seniority as you go from Emerging Leadership level 1, which is immediately post-doctoral, through to the most senior level of the professoriate, which is level 3.

So the figure on the right shows the proportion of applications, the percentage of applications, for men and women so you can see that dramatic attrition, it's true in numbers but it's clearly true in proportion, and what we see is at Level 3, there's a ratio of approximately 4 of men to women at that level every year, so the crossover point has moved a little bit in those first three years, but the ultimate ratio at the most senior level has remained at about 4 and that's a really important factor in determining the gender disparities and outcomes from this scheme as I'll come to in a minute.

[Visual of PowerPoint presentation progresses to slide with heading – Isn’t it just a matter of time? Timestamp 12:20]

So when we look at an attrition slide like that where we see women disappearing from the applicant pool and we know that that broadly reflects the shape of the workforce, although it might be a more exaggerated form of that disparity in the workforce, it's commonly said well, isn't it just a matter of time? We have all these wonderful young women coming into research in early stages of their career at EL1 and EL2 and it's just a matter of time before they'll be emerging as the applicants at L1, L2 and L3.  Well, yes, it's a matter of time, but it's taking a very long time and there are many of us, myself included, who were born in the 50s and the little pictures on the left of this slide refer to views of professional outcomes and pathways available to girls and boys in the 1950s - of course we have  researchers who were born in the decade before as well, so there are a good  many researchers in the system who grew up in a time when there were very highly gendered views about the career opportunities and pathways that were appropriate for boys and for girls and so we can see here that the boy is a potential physician, economist, engineer, et cetera, whereas the girl is more likely to want to become a nurse or a social welfare worker and she's a future Australian mother.

So we still have gendered views about the pathways that are available to boys and girls as they grow up today, but we know that the choices available are very much more diverse than the ones that were represented by this poster back in the 1950s.  But it is important to remember that we have a workforce that reflects people who have grown up and lived in a world that was very much more male-dominated across professions than it is today. So that's why the data in the table to the right of this slide are also really interesting and important.

If we look back almost 40 years to 1983, we can see that in Australian higher education institutions, undergraduates in health degrees were already more than 50% female and more than a third of undergraduate students in science degrees - not including engineering - were female.  So even almost 40 years ago we had a large group of women who were joining the health and the sciences pathways and we might have expected by now to see a higher proportion of them represented at the higher levels of our Investigator Grant scheme.

So, I think it isn't just a matter of time, it's not a matter of waiting a few more years and we'll see this problem fixed of its own accord. It is time to consider how to intervene to make this happen a little faster.

[Visual of PowerPoint presentation progresses to slide with heading – Investigator Grants – funding outcomes (2019–2021). Timestamp 15:05]

So, to get back to the Investigator Grant Scheme and the consequences of the attrition of women from the applicant pool to this scheme. They're shown here with the summary of outcomes for the first three years of the scheme 2019 to 2021, where we see that about a third more grants went to men than to women and on average about $95 million more per year went to men than to women from a total budget of $365 to $400 million.

So, our analysis of the data behind those summary data there is that the single most important contribution to that disparity in outcomes for men and women is the predominance of male applicants at the most senior level of the scheme, Level 3, and that's because the male-female ratio at L3, as I've said is about 4 to 1, or was in the first three years of the scheme. The funded rates - that is the success rates, the proportion of applications which are funded - is also highest at level 3 and the average grant size is highest at level 3. It's not designed that way, but that is in fact what the outcome of peer review is giving us.

There are differences in the average grant size awarded to men and women with slightly lower for women than men and that makes a minor contribution to the gender disparity in the outcomes overall as they're captured in that table at the top of the slide.

We have an existing intervention to improve outcomes for women and that's called structural priority funding and I'll explain how that works in a minute and it has certainly improved outcomes for women at every level of this scheme. That's already included in the data that are shown at the top of this slide.

[Visual of PowerPoint presentation progresses to slide with heading – Investigator Grants – funding framework (2019–2021). Timestamp 16:56]

So now to say a little bit more about the funding framework for this scheme because I think it is important to understand it when we consider how we might intervene to improve outcomes for women and to reduce the attrition of women from the applicant pool to this scheme.

The first thing is that, as I've said, we have three categories of grants, which we run as three separate competitions. And they have their budgets determined in advance, on the advice of Research Committee, and for the first three years of the scheme the ratio has been about 2:1:3 ??? of grants to EL1::EL2:Leadership [Investigator Grants are funded as three competitions with separate budgets (in ratio ­­~2:1:3 grants) for the three categories: Emerging Leadership (EL1), Emerging Leadership 2 (EL2); Leadership (Levels 1,2 and 3)]. That's based on the historical distribution of funds across former fellowship schemes, adjusted for various policy changes that were made when we changed over to the new grant program of which Investigator Grants is a part.

The total budget in most years, the standard total budget for this scheme is $365 million, 40% of our available funding, and that comprises $335 million which we call the baseline budget and that's simply going to be allocated straight down the rank list of applications until it runs out across the three competitions, and then $30 million within that $365 million which we describe as a structural priority budget and that will be awarded to near-miss grants, as I'll show in a minute.

The grant itself can have a salary for people who are eligible and apply for a salary - for example, Directors of Research Institutes and Deputy Vice-Chancellors of Research are not eligible to apply for their salary, but they are eligible to apply for the grant as a whole and to get a research support package. But those people who have significant administrative roles in their institutions are not eligible to get a salary from NHMRC because their work is primarily for their institution.

The research support package size varies with level. So, first of all it's fixed for Emerging Leadership levels 1 and 2, it's $50,000 per year over five years for EL1; its $200,000 per year over five years for EL2.

For Leadership grants, also five-year grants, there are four tiers of research support package that range from $300,[000], $400,[000], $500,[000] to $600,000 per annum and they are awarded based on the position of the application in the ranked list of scores.

It's a very important aspect of this scheme that we don't award those different tiers of research support package based on seniority. The decision was taken that seniority should not automatically lead to somebody getting a larger grant size, only a larger salary. But we want to talk about that some more in this discussion.

And then all of the research support packages are adjusted and they have deductions according to grants that people already hold, such as Ideas Grants or grants under the previous funding program.

And that means when you look at the actual outcomes in our spreadsheets that we put up every year on the NHMRC website, you see all sorts of funny numbers for the grant size, but that's reflecting both the combination of salary or not, different size research support package and whether or not there are deductions for other grants held.

[Visual of PowerPoint presentation progresses to slide with heading – How Investigator Grant funding is allocated. Timestamp 20:22]

So how we actually deliver the funding is, as I said we've got a budget for the scheme as a whole and then we've got budgets that are defined in advance within that for EL1, EL2 and Leadership.

We seek five independent assessments of each application and in most cases we do get five for each application.

The scores from those assessments are then used to create a ranked list in each of those three competitions: EL1; EL2; and Leadership. Then within each competition we'll go down from the top of that rank list to award grants regardless of anything except the score regardless of gender until the budget runs out and that's how the baseline budget is allocated.

We get as near as possible as we can to using up the entire baseline budget straight down the ranked list. Then we use the structural priority budget to fund additional near-miss applications in pre-defined priority areas and those are determined annually on the advice of [NHMRC’s] Research Committee.

In the first three years of the scheme that we're talking about, the four structural priority areas are the ones listed here and they're addressed in the order shown here: first of all Aboriginal and Torres Strait Islander health researchers, as CIA; then female-lead investigators and those structural priority grants are awarded in the approximate ratio of 2:1 : 3 for EL1, EL2 and L for female-lead investigators; and then Aboriginal and Torres Strait Islander health research; and health services research. We have the same structural priorities now except that we no longer have health services research on that list at the moment.

[Visual of PowerPoint presentation progresses to slide with heading – How Investigator Grants funding is allocated showing three lists headed ‘Emerging Leadership 1’, ‘Emerging Leadership 2’ and ‘Leadership’. Timestamp 22:09]

So just to illustrate how this works in practice, we have three ranked lists of applications and here we show it for the three competitions, each horizontal bar, the blue bar, is a grant and we start if we show, for example, in the case of EL1…

[Visual of PowerPoint presentation progresses to slide with heading – How Investigator Grants funding is allocated showing one list headed ‘Emerging Leadership 1 (or 2)’. Timestamp 22:23]

… we start at the top of that list, the highest ranked application is funded. We go down that list until the budget runs out and that's the red funding line. You will see, if you look at these hypothetical grants, that we've got two which are at the position 68 and both of them are being funded and if we have grants on the same score above the funding line then they'll all be funded, we'll never put the funding line through the middle of a group of grants that happen to fall on the same score.

So then we've used up the baseline budget and now we go below the line and look for the next ranked grants that address structural priority areas and we look for Aboriginal and Torres Strait Islander researchers first and then we go back to the funding line and we look for grants that are led by women. So the green grants there are the ones that are being funded as a structural priority and you can see there that there are a couple of grants on the same score, so they're ranked 70 in the list, but only the one that hits the structural priority is being funded below the line.

The same process for Emerging Leadership 2.

[Visual of PowerPoint presentation progresses to slide with heading – How Investigator Grants funding is allocated showing two lists under the heading ‘Leadership’. Timestamp 23:30]

A slightly more complicated process for Leadership because we've got the four research support package tiers to deal with, so again we start at the top of the list and we use up the budget for the highest tier of research support package and that brings us down to the yellow line, so that's in this hypothetical case that’s funded 11 grants, then we go and disburse grants with the next highest research support package and so on down to the funded line.

Now, there's a group of grants that sit above the funding line that receive the smallest research support package which is $300,000 per annum over five years and that in fact is the largest group of funded grants above the line because it's a roughly parametal distribution of research support packages through this list of grants above the line.

That's really important because then when we go below the funded line, funding line, and fund additional grants that hit a structural priority, we award them the same research support package as those immediately above the line.

They don't get less, they get the same amount as those who are immediately above the line and in fact are the largest group above the line. But it is a point of controversy because it's been suggested that people who receive the near-miss funding and get that lowest research support package are therefore to some extent disadvantaged with the average of the people who are above the line, some of whom receive a higher research support package, so that issue we’ll come back to when we talk about options for intervention.

[Visual of PowerPoint presentation progresses to slide with heading – What is the problem we are trying to solve? Timestamp 25:05]

So just to recap, what's the problem that we are trying to solve in this scheme? Well, primarily it's the pipeline. It's the attrition of female applicants as we go to the more senior levels of the scheme and then the way that plays out in many more grants going to men than to women at Leadership levels and much more overall funding going to men than to women at Leadership levels, reflecting those disparities in applicant numbers.

This we see as a reflection of the systemic disadvantage that women experience throughout their careers, and it's not offset by individual relative to opportunity adjustments. In our view this systemic disadvantage is neither offset, nor can it be offset, by the individual consideration of people's own circumstances as part of the assessment of track record relative to opportunity, where we give individuals of all genders the opportunity to describe the circumstances that have affected their productivity, whether they are positive or negative effects.

So, it's an important part of this discussion that we are positing that we need a systemic intervention to address a systemic disadvantage, while continuing of course to have relative to opportunity consideration of track record. Now, a few other things to note. We do see when we look at the scores for each assessment criteria that on the whole they're likely to be lower for women than for men, but this isn't consistent for any one criterion or for any one year of the scheme or for any level of the scheme, so we don't have a simple answer in just addressing the assessment criteria here.

We also have seen that structural priority funding definitely makes a difference for women, we’ve funded a lot more Investigator Grants for women because of the structural priority funding initiative, but it hasn't fully solved the problem, as the overall data show.

And then finally, there's this point that has come up in the sector that the Leadership grants awarded through near-miss structural priority funding receive the lowest support package at that Leadership group.

[Visual of PowerPoint presentation progresses to slide which reads – Go to www.menti.com and use the code 9315 9881; Your feedback. Timestamp 27:23]

So we're now going to move to Menti, and this is a chance to start to ask you a few things about yourselves and then we're going to be asking your opinions at a number of points through the rest of the presentation.

[Visual of PowerPoint presentation progresses to slide which reads – Go to www.menti.com Enter the code 9315 9881; Or use QR code. Timestamp 27:37]

So Prue is going to put up the screen here so you can use the QR code or you can go to menti.com and enter the code there, and that code 93159881 will remain at the top of the screen for all the Menti questions that we're going to show throughout this talk, if you need to put it in again.

So we'll give you a moment to enter this.

[Visual of PowerPoint presentation progresses to slide with heading – How would you describe your gender? Timestamp 28:07]

And then I should be clear that your answers here are going to be anonymous. We won't know who you are. We will be correlating answers throughout the Menti session, so if you now tell us your gender, we'll be able to compare your gender with the other answers and we'll show that for the next question, but we won't know who you are.

So, the first question, which I hope is the easy one, is how would you describe your gender? And we'll wait for long enough that people have entered this before we show the outcome.

[Visual of PowerPoint presentation updates slide with heading – How would you describe your gender? with survey outcomes. Timestamp 29:06]

OK. So, we have a great majority of women in this audience, as has generally been true as we go around the country.

[Visual of PowerPoint presentation progresses to slide with heading – What is your career stage under the Investigator Grant scheme? Timestamp 29:21]

So, the next question asks how you would describe your career stage under the Investigator Grant scheme and some people who are attending may already hold Investigator Grants so you know what level you've been awarded at. There will be others who are applying or planning to apply next year, so you'll have a fair idea of where you sit on the scale, but for others for whom you're not sure or it's simply not applicable, please choose those options and we'll display these results against gender.

[Visual of PowerPoint presentation updates slide with heading – What is your career stage under the Investigator Grant scheme? with survey outcomes. Timestamp 30:23]

Okay, it's good, we have a good distribution of people attending this webinar, so that's great, thank you very much.

[Visual of PowerPoint presentation progresses to slide with heading – What is the key factor that causes gender disparities in NHMRC’s Investigator Grant scheme? Timestamp 30:34]

Now, the third and final question in this set - we'll have other questions later - asks you "What is the key factor that causes gender disparities in the Investigator Grant scheme in your view?”, you can put more than one answer and it will be displayed as word cloud, so you can use words or phrases.

[Visual of PowerPoint presentation updates slide with heading – What is the key factor that causes gender disparities in the Investigator Grant scheme? with survey outcomes. Timestamp 32:15]

Great, thank you very much.

[Visual of PowerPoint presentation progresses to slide with heading – Options for gender equity in Investigator Grants. Timestamp 32:24]

So now we're going to move on and talk about possible interventions and as you may know if you've read the discussion paper we released back on the 15th of July, we have modelled four options for major intervention in the Investigator Grant scheme. Now, obviously these aren't the only possible things we could do and we could also consider variations on the themes that are represented by these four options, but we chose these to test thinking and to provide some clear data on what the outcomes from previous grant rounds would have been if we'd used any one of these options.

So the first two options are 1 and 2 are based on the existing structural priority initiative. As you know, as I've said, we normally have a $365 million budget for Investigator Grants, of which $30 million is for structural priority funding, so that's just over 8% of the total budget. So the first two options here propose increasing that budget to 20% of the total budget. It's not increasing the total budget available for Investigator Grants, but it's increasing the proportion of the budget within that $365 million that will be available for structural priority funding and therefore reducing the baseline budget that's awarded regardless of gender.

So that's true for options 1 and 2, but option 2 has an additional element, which is to award a single research support package size for all Leadership levels. As I said, we currently award four different tiers of research support package for Leadership levels that's awarded based on position in the ranked list and those packages range from $300,[000] to $600,000 per annum.  $400,000 per annum is the nearest of those package sizes to the average that we award across the Leadership levels. It's a little bit above the average, so it means you get slightly fewer grants with a $400,000 package for everybody, but it does give us the simplicity of a single package and addresses that issue that people have raised about those who are funded through structural priority funding receiving a lower package than some of those above the line.

Options 3 and 4 are what we've described as breakthrough initiatives because they go directly to seeking parity. Option 3 awards equal numbers of grants by gender to men and women, talking in the binary as I've already said, and option 4 awards equal total funding to men and to women.

We also, as part of this discussion, are considering how to look after, how to consider non-binary applicants and applicants who prefer not to state their gender identity at all.

The first thing that we will do is collect more information. As I said earlier on, at the moment we only ask applicants to voluntarily check a box that says male, female or other and they can choose not to answer. We're going to ask for more information about gender identity and there's more detail about this in the discussion paper that we put out on the 15th of July. So that's what we will do, and then what we're considering doing is to include non-binary applicants in any gender equity initiative that we introduce to support women, so we would include - we would have non-binary applicants and female applicants in the same initiative.

Now, I'm not going to say anything more about this in the presentation, but as I said before, we're very happy to talk about this further in discussion if you'd like to. So, from now on I will just continue to talk in the binary for the purposes of this discussion.

[Visual of PowerPoint presentation progresses to slide with heading – Modelling the options. Timestamp 36:15]

So the models - a couple of other things about the modelling. First of all, what we've done here is use the actual applications and their peer reviewer scores from the first three rounds of the scheme, 2019 to 2021, to ask what would the outcomes have been if we had distributed the funds using any one of these four options?

It's really important that this is not necessarily predictive of future years because in future years we'll have different applicants, they'll get different peer reviewer scores and the shape of the sector and the behaviour of the sector can change over time, and so it's a bit like your superannuation fund saying past performance is not a predictor of future performance, but what we can say here is this is what would have happened in the previous three years if we'd used any one of these four options.

We're not talking about the 2022 round because that is just wrapping up at the moment, so applicants will know that the outcomes of that round are not yet released to applicants, they're still going through approval processes, and so we can't talk about those data yet.

Now, they're not stated and other applicants that are part of the applicant pool in 2019 to 2021 have been excluded from this modelling for simplicity, but of course they will never be excluded in actual outcomes.

When we talk about actual data in the slides that I'm going to show next, we're talking about the actual outcomes of the scheme in those first three years as they have been published on NHMRC's website and so there are Excel spreadsheets of outcomes there with every grant listed, but there's also each year a fact sheet about Investigator Grant outcomes, so actual data are the ones that actually occurred and are published and public. Finally, in the modelling we have simply funded Indigenous Chief Investigators exactly as they were funded in the actual outcomes. That's again for simplicity, and it's important to be clear that if we were to proceed with any one of these options, in particular option 3 or 4, which do not refer to structural priority funding, we would still use structural priority funding to award grants to Aboriginal and Torres Strait Islander CIAs below the line.

[Visual of PowerPoint presentation progresses to slide with heading – Modelling methods. Timestamp 38:41]

So how have we done these models? First of all, for options 1 and 2, which increase structural priority funding to 20%, this shows the background to how we get the numbers. So in 2019 and 2020 we had a $365 million budget each year and of that we had $335 baseline budget and $30 million structural priority budget as shown there under actual, so to determine 20% structural priority we would simply remerge those two pots back to $365 and now 20% structural priority budget becomes $73 million instead of the $30 million that was actually used.

2021 was a bit different because we used whatever funds we could draw in, including funds that have been delayed in their disbursement during 2020 because of the pandemic, so we used whatever funds we could gather for Investigator Grants and Ideas Grants to improve funding to the sector in consequence of the pandemic.

So, we had the $365 million baseline budget or $335 baseline and we had the $30 million structural priority budget, but we had some additional structural priority funding of $11 million, we had some additional funding for EL1 and EL2 of $15 million and additional special dementia funding of $9 million we also directed to EL1s and EL2s undertaking dementia research. So we had in 2021 at baseline for the structural priority modelling of $292 and then $84 million in structural priority funding, so that's how the numbers were derived. For anyone who's interested but is probably not important for what I'm now going to show.

[Visual of PowerPoint presentation updates slide with heading – Modelling methods. Timestamp 40:26]

So then for models 3 and 4, which are the ones that deliberately seek to achieve some parity by a couple of different criteria. In this case, what we've done is take our normal three competition budgets and then further split them into male and female budgets and then – to/or grant numbers. And then, for option 3, go straight down each list of women and men and fund one grant for a woman, one grant for a man, one grant for a woman, one grant for a man, in each of the three competitions until the budgets are used up. And so the intent there is to end up with equal numbers of grants going to men and women across the three categories. You'll see when we look at the modelling data that they don't always come out exactly equal and that's because we did in the modelling what we would normally do in real life and that is use up every last dollar of the budget that we can, and so sometimes you end up with a few more grants going to one gender than the other or a few more dollars going to one gender than the other. So that's model 3, option 3.   

For option 4, to model that, we split the budgets in two in the three competitions and then we simply ran down those three competitions until the budgets ran out, so we've got separate budgets for men and women. So I hope that's clear, but I'm happy to answer questions about it later if it's not.

[Visual of PowerPoint presentation progresses to slide with heading – Comparison of actual and modelled outcomes displaying six figures under heading ‘EL1,EL2 and Leadership categories (2019–2021). Timestamp 41:51]

Now, I want to go to looking at the outcomes of the modelling and I'm going to show a series of slides that are based on Figures 11 and 12 that are in the discussion paper we released on the 15th of July. The top panel in each case comes from Figure 11 and the bottom panel comes from Figure 12.

I'm going to talk about EL1, EL2 and Leadership categories, the three separate competitions first, and then come to look at within Leadership L1 versus 2 versus 3.  And you'll see on every slide we have the total number of grants on the top panel and the total funding commitment on the lower panel. And something you'll notice here and in the subsequent slides is that, broadly speaking, although the height of the bars is different, the general pattern is very similar, whether you look at number of grants or number of dollars because the average grant size is very similar. So if you just want to look at the top one, it's probably easier and will give you the general picture.  

So first of all, just to talk about EL1 and EL2 and to look at the actual outcomes…

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the Actual EL1 and EL2 data. Timestamp 42:55]

…those are circled here. So these are what we have published on our website in 2019, 2020 and 2021. If we look first of all at Emerging Leadership 1 on the left, you can see that the actual outcomes were that women won slightly more grants over the three years than men and it doesn't show very well, but slightly more dollars as well, overall, over the three years. If we look at EL2, the heights of the two bars for male and female applicants is very similar, so at EL2 we're already effectively at parity both in the number of grants and the total funding commitment to EL2.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the data for Model 1 and 2 for the EL1 and EL2 figures. Timestamp 43:47]

If we then look at the impact of the two options, models 1 and 2, which are 20% structural priority funding and then a single research support package for Leadership, we see that for EL1 we increase slightly the number of grants awarded to women under both of those models and slightly increased the number of dollars awarded to women than to men as well. It's a modest effect, but there is a little bit of an effect. At EL2 it doesn't really make much difference, we're basically at parity already, it slightly adjusts the numbers, but overall it makes very little difference.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the data for Model 3 and 4 for the EL1 and EL2 figures. Timestamp 44:21]

If we look at models 3 and 4, we see – and these of course deliberately attempt to make equal numbers of grants or equal number of dollars to men and women and you can see the bars are roughly the same size but as I've already explained, they're not exactly the same size because we are using up every last bit of the budget and so you can end up with an unequal number of grants or an unequal number of dollars. So, of course, by definition models 3 and 4 achieve rough parity for EL1 and for EL2 and again for EL2 just look across the board, it's not really making much difference overall.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the Actual Leadership data. Timestamp 45:03]

So to turn to Leadership, where the differences are much more striking, and now we see for the actual outcomes many more grants and many more dollars to men than to women on average over those first three years of the scheme.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the data for Model 1 and 2 on the Leadership figures. Timestamp 45:17]

If we look at the impact of models 1 and 2, 20% structural priority funding, we slightly bridge that gap. We do increase the number of grants to women and the number of dollars to women and we therefore accordingly decrease the number of grants and number of dollars to men.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes with circles around the data for Model 3 and 4 on the Leadership figures. Timestamp 45:37]

When we look at models 3 and 4, remembering again that these are deliberately attempting to equalise across the whole category of Leadership, then that’s what we see. They’re roughly equal sized bars, heights of bars, and what we also see is that they lead to an increase, a further increase, in the number of grants and the number of dollars to women and a further decrease in the number of grants and dollars to men.

So that’s the picture overall for Leadership category and I’m now going to show the outcomes within Leadership at the three levels of L1, 2 and 3, remembering that it’s a single competition at Leadership. We’re not having separate budgets for L1, 2 and 3. 

[Visual of PowerPoint presentation progresses to slide with heading – Comparison of actual and modelled outcomes, showing data for Leadership levels 1, 2 and 3. Timestamp 46:18]

So the outcomes are really important and interesting. So now we’ve got exactly the same layout of slide, but we’ve got L1, 2 and 3 across from left to right.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes, with circles around the Actual grants for L1, L2 and L3 data. Timestamp 46:31]

First of all for actual outcomes, we can see at L1 that there are more grants and more dollars to men than to women, at L2 that disparity is slightly higher, at L3 it’s very much higher, and that disparity at L3 essentially reflects the application ratio. So I’ve pointed out earlier on that the ratio of applications from men to women is about four [to one] across the three years of the scheme at L3. And we're seeing roughly similar ratio in the number of grants awarded to men and women at L3 and also in the number of dollars. So the impact then of the models is going to be different depending on whether we look at L1, 2 or 3.

[Visual of PowerPoint presentation updates slide with heading – Comparison of actual and modelled outcomes, with circles around Model 1 and 2 data for L1, L2 and L3. Timestamp 47:18]

So if we look at models 1 and 2 first of all, 20% structural priority funding and single RSP, at L1 we increase the number of grants to women and reduce the number of grants to men, similarly for dollars. At L2, we have rough parity. At L3, we have a slight reduction in the gap, but the gap is still really large, but it is reduced.

[Visual of PowerPoint presentation updates slide with heading – Modelling the options with circles around Model 3 and 4 data for L1, L2 and L3. Timestamp 47:43]

And then when we go to models 3 and 4, we see a greater increase, a really substantial increase compared with actual in the number of grants at L1 to women compared with men. At L2 we've gone slightly beyond parity, so we've almost reversed the ratio and at L3 we have further increased the number of grants to women and further decreased the number of grants to men, but clearly there's still a long way to go.

So it's as though this huge differential in application numbers at L3 is just a bridge too far with the way the scheme is run and the way these models have been implemented with the data.

[Visual of PowerPoint presentation progresses to slide with heading – What about merit? Timestamp 48:28]

And then as soon as you talk about these sorts of interventions, of course the question comes up about merit. NHMRC always says it's funding excellence, that's what it seeks to do, so if we're talking about adjusting on the basis of gender, what does that mean about the merit, are we reducing our reliance on merit as the basis for awarding NHMRC funding?

So we thought it was very important to look at what each of these models would do to the lowest funded score, or the funding cut-off, for men versus women under each of those options. I've only shown the data here for 2021. It's the most recent year and also, you know, it's a lot of data to show on one slide if we show all three years, but the detailed data for each year is presented in the discussion paper released on 15th of July if you want to look.

So what we see in general is, what we're showing here is, the difference in the funding cut-off compared with actual outcomes, so actual outcomes is zero and then you can see for most of the female options that we drop below that zero line because we somewhat lowered the funding cut off for women applying in this scheme, at the various levels, and conversely we're seeing a slight rise in the cut off of the lowest funded score for men, under each of these options at the three levels of the scheme. And it depends on the level what difference you see.

What's really striking is that the biggest differences are seen, away from zero, are seen for women and men in options 3 and 4 and what we also note from this and at the Leadership level, I should say - and what we also note from this is that the extent of those differences is always under 0.15. So we're talking about a 7-point scale where the funding cut off is shifted by up to about 0.13 by options 3 and 4, downwards for women and upwards for men. So, 0.15, 0.13 out of a 7-point scale.

Importantly, also what this means is that the lowest funded scores remain above 5 for all applications that are funded under these four modelled options and that means they are all tagged at least excellent and in fact most of them remain above 5.5 and so they're outstanding, under NHMRC's rubric. So, while there's a small impact on what we might use as a measure, what we consider as a measure of merit, it is a very small impact compared with the overall distribution of scores.

[Visual of PowerPoint presentation progresses to slide with heading – Conclusions from modelling the four options. Timestamp 51:21]

So what can we conclude? Well, for options 1 and 2 they certainly increase the grant numbers and the total funding for women and if we look at the Leadership category overall, they reduce the gap between men and women, but they don't eliminate that gap completely. Options 3 and 4 by their very nature achieve nearly equal grant numbers and total funding across the three competitions, EL1, EL2 and Leadership, But when we look within Leadership we can see they reduce, but don't eliminate, that very big gap at Leadership level 3. For all of the options we can see that actual outcomes were already close to equal or favoured women at EL1 and EL2. No option could bridge that substantial gap at L3 and the funding cut offs under all four options remain in the excellent and outstanding score categories.

[Visual of PowerPoint presentation progresses to slide which reads – Go to www.menti.com and use the code 9315 9881; Feedback on the options. Timestamp 52:14]

Now, we're going to go to Menti again and now we're going to ask you your thoughts on each option in turn. We'll ask you later about which options you think are best, so this is not about comparing the options, it's simply your reaction to each option in turn and whether you would support it if we elected to implement it.

[Visual of PowerPoint presentation progresses to slide with heading – Option 1: Increase structural priority funding to 20%. Timestamp 52:37]

So, the first one is what do you think about option 1, increasing structural priority funding to 20%, do you agree or not?

[Visual of PowerPoint presentation updates slide with heading – Option 1: Increase structural priority funding to 20% with survey data. Timestamp 53:39]

Okay, thank you very much. 

Now we'll ask the same question for option 2, which is increasing structural priority funding to 20% and having a single Research Support Package of $400 K per annum for Leadership.

[Visual of PowerPoint presentation progresses to slide which reads – Option 2: Increase structural priority funding to 20% and award a single research support package (RSP) ($400,000 per annum) for all Leadership levels. Timestamp 53:47]

[Visual of PowerPoint presentation updates slide with heading – Option 2: Increase structural priority funding to 20% and award a single research support package (RSP) ($400,000 per annum) for all Leadership levels with survey data. Timestamp 54:41]

Thank you very much.  That's helpful.

Now we'll ask the same question for option 3, which is equal numbers of grants by gender.

[Visual of PowerPoint presentation progresses to slide with heading – Option 3: Award equal numbers of grants by CIA gender. Timestamp 54:49]

[Visual of PowerPoint presentation updates slide with heading – Option 3: Award equal numbers of grants by CIA gender with survey data. Timestamp 55:49]

Thank you, that's striking. Let's move to option 3[4], which is equal total funding by gender.

[Visual of PowerPoint presentation progresses to slide with heading – Option 4: Award equal total funding by CIA gender. Timestamp 55:58]

[Visual of PowerPoint presentation updates slide with heading – Option 4: Award equal total funding by CIA gender with survey data. Timestamp 56:56]

Okay, thank you very much.

So now we move on.

[Visual of PowerPoint presentation progresses to slide with heading – Concluding comments. Timestamp 57:05]

I'm going to make a few concluding comments and then we're going to ask you some more Menti questions that are more about comparisons between the four options that we've talked about today. Now, these four options are presented as potential interventions to offset the systemic disadvantage faced by women as they progress through their health and medical research careers. As I've wanted to emphasise, the options were modelled on past grant rounds and so we can't be certain that this is how it would work out if we introduced any of them for future grant rounds, each round is different.

Options 3 and 4 are very similar in their impact because their average grant size has been close to the same in the first three years of the scheme, and both of those options have a greater impact than options 1 and 2. A really important point for us to think about is that we're talking about a fixed budget for this scheme. There's no magical pot of money that could give us another $100 million a year or whatever, so within that fixed budget any policy to increase funding for women would lead to a decrease in funding for men. All the options that are presented here would rebalance funding in favour of women. It does mean that more women will be able to pursue their research goals and advance their careers. These are five-year grants with a package to support them and so they do make a difference to people's career advancement. It pushes it on another five years, another five years of opportunity. And this would help reduce attrition of women from the sector and increase the visibility of women as researchers in the sector.

But we're very clear at NHMRC that we can't solve this systemic problem on our own. Of course NHMRC is an important funder in Australia and it is an influential funder because our policies do affect people's behaviour and they affect institutional behaviour as well.But it's really clear that what we do in one scheme cannot fix a decades old or centuries old problem and that this is going to require action at every level of the system.

This is why, as we've gone around the country over the last couple of weeks, we've held meetings with senior institutional leaders because we want to understand what institutions are doing to address this issue. We recognise that institutions are powerful in this space because they make decisions about who to recruit, who to attract. They then make decisions to employ, they make decisions about resources that are available and the types of jobs that people will do in their   institution, and they may make decisions about who will leave the institution.So they have tremendous influence also on gender outcomes as women progress through their careers, whether they be in universities or institutes or hospitals or elsewhere.

So those are issues that we must collectively address, but NHMRC is very keen to do something and to use what influence we have to do something that's constructive for the sector as a whole and for Australian health and medical research.

So we're now going to go to a few questions that ask your views about the relative merits of the four options that we've presented, remembering that we don't only have to consider these options and we will ask you about possible variations.

[Visual of PowerPoint presentation progresses to slide with heading – Which option is best for the future of Australian health and medical research? Timestamp 1:00:31]

So, first of all, which option is the best for the future of Australian health and medical research, option 1, 2, 3 or 4? If you don't think any of them are best or good, don't answer the question.

[Visual of PowerPoint presentation updates slide with heading – Which option is best for the future of Australian health and medical research? with survey data. Timestamp 1:01:30]

Okay, thank you.

[Visual of PowerPoint presentation progresses to slide with heading – Which option is best for gender equity in outcomes? Timestamp 1:01:39]

Now, the next question is which option is best for gender equity in outcomes?

[Visual of PowerPoint presentation updates slide with heading – Which option is best for gender equity in outcomes?? with survey data. Timestamp 1:02:31]

Thank you.

[Visual of PowerPoint presentation progresses to slide with heading – Which option is the most proportionate and fair? Timestamp 1:02:39]

And now the final question of this type is which option is the most proportionate and fair?

[Visual of PowerPoint presentation updates slide with heading – Which option is the most proportionate and fair? with survey data. Timestamp 1:03:33]

Okay, thank you.

Now, I want to ask you about possible variations on these themes and

[Visual of PowerPoint presentation progresses to slide with heading – Which adjustments to the options do you support? (multiple selections allowed). Timestamp 1:03:43]

so this question is which adjustments to the options would you support? And you can choose more than one of these and if you don’t think we should consider any of them, then there’s no need to answer.

[Visual of PowerPoint presentation updates slide with heading – Which adjustments to the options do you support? with survey data. Timestamp 1:04:48]

Okay, thank you very much. Now, that’s the last Menti question, but we ask you to stay on Menti because we’re going to ask you to submit your questions that way.

So now if we go to the next slide, I just want to tell you where we’re up to in this consultation and what happens next.

[Visual of PowerPoint presentation progresses to slide with heading – Consultation timeline and next steps. Timestamp 1:05:04]

This really began at the beginning of the year when we produced the CEO Communique that was posted on our website in February. And it's a detailed analysis of funding outcomes in the first three years of the Investigator Grant scheme and some of my comments today and the data I presented come from that Communique.

We then held a couple of webinars in late February and early March to talk about the issues and we had really fantastic attendance and feedback from researchers around the country on the issues and many people told us about their personal circumstances and we could get a good appreciation of what this really means at the level of what it's like to be a researcher in the sector.

So then we commenced modelling the options that I've discussed today and in order to have time to be able to discuss these with the research sector and give some consideration to the feedback, we delayed the opening of the next round of Investigator Grants so that we could undertake this consultation and still have a chance of implementing something for the next round, which will open in January.

So that next round - the application period has been delayed and the peer review period has been delayed, but the onset of funding will still be January 2024, so there's no delay to the ultimate award of those grants.  We then released the discussion paper and an online survey on the options on 15th of July.

That survey closes at 5 o'clock today because this really is the last day of public consultation on these options, and the online survey asks many of the same questions we've asked through Menti today, but also gives the opportunity for people to answer some other questions and to provide some free text comment as well, so we've got a really rich source of data coming to us from Menti at these sessions and also from the online survey.

We then have had a period of consultation over July/August and in late July we met with a number of peak bodies such as Universities Australia, GO8 and the Association of Australian medical research institutes, ASMR and others, so that was a really useful kind of feedback from a peak body point of view and then we've been going around the country, as you know, finishing today giving presentations like this one and having this Menti approach and also then having a question and answer session as we're about to have for this session as well.

While we were going around the country, we also held meetings with senior institutional leaders from quite a number of institutions, not all of course but from many, and that was a great opportunity for us to hear about initiatives that institutions are pursuing to address gender equity in their own research workforce. And we were very impressed by the level of engagement and activity around the sector in this area and reassured that we can all work together to address this issue across the whole sector.

So the next steps are, as we finish up this consultation later today, is to review feedback from the survey from the meetings we've had with institutional leaders and peak bodies and the feedback through the open forums like this one today.

We'll then have a brief period of consultation with our advisory committees and government and then we need to make a decision and subject to that decision, we will decide whether to change the 2023 Investigator Grant Guidelines.

Now, the reason we are doing this on such a short timeline is because if we want to have changes to the Grant Guidelines for the next round, we need to have those changes in place in September because they then have to go through several rounds of approval within government through Department of Finance, PM&C, and then ultimately the Minister for Health before they can be released to the sector, which of course needs to happen before the scheme opens in January, so that's our proposed timeline.

Now, if we cannot make a decision or if we think we don't have social licence from the feedback we've received to make a decision at this point, then there will be a further delay and we won't be able to do anything for 2023 and we'll be thinking about what to do for 2024. Assuming we can do something for 2023, then we will proceed with changes to the guidelines and getting the necessary approvals and at some point before the scheme opens we would be reporting back to you in the sector about what we've decided to do, why we've decided to do it and we'll also distribute summaries of the outcomes of the feedback we've had through Menti surveys and the online survey.

[Visual of PowerPoint presentation progresses to slide with content – Thank you. Timestamp 1:09:57]

So I'd like to close there and I thank you very much for all of your attention and your participation in the Menti survey. As I said, it would be great if you could stay on Menti as a way to tell us the questions now and Caroline, as our Council chair and facilitator for this session, is going to help with now managing a Q&A session with I think quite a number of people online. I do hope we can address most of the questions. I can imagine it may not be possible to address them all, but we will do our best. So, thank you very much and back to you, Caroline.

[Presentation ends. Timestamp 1:10:35]

End of transcript.