The loss of women from scientific research is well documented: while women make up approximately 50% of PhD students within STEM (depending on the research field), the proportion of women quickly drops off at higher levels of career progression within research (e.g. 18% of Professors in STEM in the UK and 17% of biology-related professors in the US. Many drivers of this loss have been described, including gender stereotyping, implicit bias (e.g. here, here and here), explicit bias, desirability of research careers and self-selection. A major perceived barrier to a scientific career for women is having children, with associated career interruptions and impacts on track records. Some of these drivers can be addressed relatively easily; others less so. Specific training can help reduce implicit bias, while increasingly in Australia and many other places, research track record is now (supposed to be) judged relative to opportunity, i.e. the number of years actually worked.
In an article published in Science today, I give some tips for writing a CV and research track record that accounts for opportunity, with the aim of making it easier for the reader, such as grant assessors or job panels, to recognise the achievements of a researcher whose career has not followed the assumed norm of full-time and uninterrupted research. While I write largely from my own experience as a mother of three boys, the same principles apply to those whose careers have been interrupted for health reasons, and other caring responsibilities.
My main point is that it is critical is to write explicitly and clearly about career interruptions, and make it easy for the reader to see what you have achieved during that time, for fair comparison with others. If you hide career interruptions or only mention them, then it will just weaken your apparent track record.
Tip 1. Get the data: Work out the proportion of time worked. For example, I have an excel spreadsheet with the percentage full-time equivalent (FTE) worked each month of my career, post PhD: 1 when I’m full time, 0.6 when working 3 days a week, and 0 when on maternity leave. It’s a good idea keep this up to date to make it easy to work out FTE when applying for grants or jobs. Next, work out what you have achieved in that time (you’ll be pleasantly surprised!), such as publications by year, citation rates, authorship status, etc.
Tip 2. Do the maths: Rather than hope the reader does it for you, work out what these would equate to had you worked full-time, presenting these data in the light of career interruption. This includes using multipliers. For example, if you have worked on average 60% during the last 5 years (=3 years FTE), and have had 10 publications in that time, this is equivalent to 17 publications (16.7=10/0.6) over 5 years had you worked full time. This can also be done with citations and other metrics – assuming you would have published more but equally good papers during your career interrupted time.
Tip 3. Don’t hide career interruptions – write about them upfront and in a positive way: State what you have done at the top. I put it on the first page of my CV, and always include FTE in my metrics/career summary (e.g. in grant applications) to make it easy to readers to find it and work out your value.
For example, in my CV, I have career interruptions explicitly listed on the front page, in the section on positions held. For example: “Career interruptions: I have the equivalent of 5.6 full-time years of post-doctoral research experience over 8.5 calendar years. I have three children (born March 2009, March 2011 and June 2013, 8 months maternity leave with each), and have worked part-time since. I have worked 3.3 years (55% full-time equivalent) since January 2009.”
I also include summary statistics including FTE multipliers in my summary of research achievements on the first page of my CV, and state my achievements during and post-career interruptions. For example: “I have 30 peer-reviewed publications in leading peer-reviewed international journals; 23 of these were published in the last 3.3 years full-time equivalent, since 2009.”
And I do the maths for easy comparison with others. In grant applications, I always include FTE metrics in my achievements, and tell the reader what that means in terms of track record; for example:
“Since 2009, I have worked the equivalent of approximately 3.3 full-time years, an average of 55% of full time. Yet it has been a highly productive period: 23 publications—including 12 as lead or last author—a research fellowship, and a major grant. On a pro-rata basis, that equates to 42 publications in 6 years of full-time work. This is exceptional in the face of career interruption.”
But I also like to emphasize that reduced working hours is not everything – working part-time and having kids isn’t easy (while trying not to sound like I’m complaining too much – not an easy balance to strike) – but I’m still doing reasonably well:
“This does not account for the reduced networking opportunities, impacting collaborations and citation rates, as a result of reduced time at work and limited ability to travel. Nevertheless, I have developed substantial international networks, and my research has both scientific and practical impacts”.
I want the reader to think, if she managed this working part-time, with breaks and sleep deprived/exhausted, imagine what she can do when the kids grow up a bit.
Conclusions: Although metrics such as the number of publications, H-index, citation rates, impact factors, and grant income have come under substantial (and oft warranted) criticism as the main measures of academic success (here, here and here), these metrics are used to judge and compare researchers. Whether we like them or not, we need to make them workable for everyone, regardless of their path through science, so that the comparisons between researchers are fair and bias reduced. I hope these modifications might help along that path. Good luck!
Ref: Nicholson, E. (2015) Accounting for career breaks. Science, 348, 830 [link]
Update: I am so pleased that this article has been well received and, more importantly, well read: it is the most downloaded/read article in Science in the week after publication, and has done very well in Altmetrics (top 4% of Science articles and top 1% of all articles evaluated by Altmetrics!). i hope it helps more than a few people out.