PhD productivity vs. having a life – both please!

Scientific research is pretty competitive, especially when it comes to securing funding. As such, our work is never truly done and to-do lists can feel endless. These two things frequently result in researchers working extremely long hours. A fascinating article on this subject was making the rounds on Twitter last week. In the post, a tenure track researcher at Harvard University mentions that some people believe that success in academia can only be achieved by working 80 hours/week. She points out that this “would mean ~11 hour work days all 7 days of the week. That’s crazy, and *completely* unreasonable”. I agree wholeheartedly.

This workaholic mentality is quite pervasive though, both in academia and beyond. PhD comics frequently illustrate the absurd idea that PhD students are expected by their supervisors to dedicate all their time to their research (see a recent comic here). Although the comics are fortunately an exaggeration, many PhD students do feel the pressure to work long hours to compete for the precious few post doc positions out there. I’m reminded of this excellent article by Scicurious from a couple years ago, commenting on the many problems with fostering this kind of attitude in early career researchers.

(Source)

Surely working excessively long hours is counter-productive. After a certain amount of hard work and mental effort, one’s ability to concentrate and the quality of work decreases dramatically. Our brains need adequate sleep and nutrition in order to perform optimally. Similarly, relaxing, spending time with family and friends, exercising and doing hobbies are necessary to stay mentally healthy and combat stress. A quick route to an unhealthy and overly stressful lifestyle involves trying to get by on caffeine rather than getting enough sleep (see this fascinating article), repeatedly missing out on social events and skipping meals or eating take-away/microwave meals.

There is no question that we need a work-life balance. The question is, can you succeed in science by working “only” 40-45 hours a week? Where I work, most people go home by 6pm and do not lament or brag about working at weekends. I occasionally receive work emails that have been sent on weekends or late in the evening, suggesting that at least some of my colleagues work from home outside of core office hours. I certainly check and reply to emails/do other work from home on occasion too. However, I strongly doubt that anyone in my research group regularly works more than 50 hours/week. And yet, as a group we seem to be reasonably scientifically productive, healthy and happy.

Just to clarify, I completely understand the occasional need to work longer hours in order to get something important done or to meet a deadline. Friends approaching their PhD thesis submission deadlines definitely put in more hours of work a week than average. Similarly, professors writing grants clearly also work longer hours in the weeks before submission. These are understandable exceptions though and they happen for a finite time period.

Perhaps I am really lucky to have found myself in such a healthy work environment or maybe I am incredibly naïve and underestimate how long my colleagues work. Either way, I would much rather work efficiently for 40 hours a week and take the time to recharge my batteries. There are plenty of technological tricks that can increase productivity and cut the length of time tasks take. For example, most statistical packages (Stata, R, SPSS etc.) allow one to use do-files/scripts/syntax to easily run, annotate and repeat analyses. Referencing programs (Endnote, Papers for Windows, Zotero or Mendeley) can cut out days of work, particularly when you need to re-do references after having an article rejected and preparing it to re-submit somewhere else. Microsoft Word has many built-in options that make working with long word documents a breeze (e.g. formatting styles, automatic table of contents). Learning how to use software fully and efficiently (i.e. understanding all the quirks and options) is an investment of time that will pay out exponentially.

In my (as yet limited) experience, I fail to understand how working 50+ hours a week can lead to increased scientific productivity that is worth the cost to one’s physical and mental well-being.


A Framework for Mental Health Research (RDoC)

Although the Research Domain Criteria project (RDoC)  is not particularly new (the description of it on the NIMH website is dated June 2011 and it’s been on my reading list for at least a year), there has been a lot of attention drawn to it recently. This is partly because the DSM-5, the new psychiatric diagnostic handbook, is due to be published on May 18th, prompting Thomas Insel, director of the National Institute for Mental Health (NIMH), to recently write about the necessary next steps in mental health research (see here and here). I’m sure many others (for example The Neurocritic) have written about this recently. Having finally gotten around to reading about RDoC on the NIMH website  myself, I wanted to briefly summarise what it is and why it’s brilliant. If you haven’t heard of RDoC or keep meaning to look it up, this is for you.

The RDoC project is a framework for thinking about and researching all aspects of human psychopathology/mental health, without confining the research to existing diagnostic labels. The DSM-5 is the best thing we currently have for the purpose of clinical assessments and diagnosis, in the hope of trying to treat and improve the lives of people with mental health difficulties. But research suggests that it isn’t good enough.

In reality, mental health conditions overlap greatly, both in terms of clinical presentation, associated features (e.g. cognitive difficulties) and in terms of apparently non-specific risk factors (e.g. many genetic variants have now been shown to play a role in more than one condition (e.g. schizophrenia, autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), all previously thought to be quite distinct conditions)). Even within a diagnostic category, there is a lot of variability in severity and also, you don’t need a diagnosis to have problems; symptoms below diagnostic thresholds often cause difficulties for affected people. It has become clear that mental health problems are not binary. Instead, there appears to be a continuous distribution of mental health difficulties, ranging from none to very severe.

The RDoC framework is about cutting across these diagnostic labels and looking at the underlying dimensions of behaviours and measures of neurobiology. The idea is that recruiting participants to a study on mental health based on their diagnoses and then trying to determine how they differ from “healthy” controls in order to inform research on diagnoses, is actually a little circular. The alternative approach, suggested by the RDoC framework, is to recruit participants with a range of related problems (e.g. all types of mood disorders) and look within this group.

RDoC

(Example of the 2 main dimensions of the RDoC Framework)

The framework divides up mental health into a list of constructs (such as ‘Responses to potential harm (Anxiety)’, ‘Reward Learning’, ‘Cognitive Control’ or ‘Social Communication’) within more general domains (e.g. Cognitive Systems). It then divides research approaches into units of analysis (e.g. Genes, Molecules, Cells, Observed Behaviour etc.). Two other dimensions are ‘Developmental Aspects’ (how these constructs change over time) and ‘Environmental Aspects’ (how the environment affects and interacts with the constructs).

The hope is that considering mental health in terms of these dimensions rather than diagnoses, will serve as a research framework for improving our understanding of mental health and creating better diagnostic categories for the future. This framework seems to me a much more valuable way of doing research in this area. It reminds me of a great blog post by Dorothy Bishop from 2010, in which she argued that neurodevelopmental problems should be considered on a number of developmental dimensions, rather than as discrete clusters of difficulties (i.e. diagnostic categories). I was very inspired by this way of thinking when I first started my PhD and so I think it’s great to see that the NIMH is encouraging researchers in mental health to adopt this way of thinking.