Today's doctors: What do men and women value in a training post?

Objectives Recent studies suggest that traditional male-female differences may be changing in terms of what is valued in a medical career but there have been no studies directly quantifying the relationship between gender and stated career-related preferences. To address this gap, we examined the differences between male and female doctors in terms of the strength of their work-related preferences at the point of eligibility to enter residency or specialty training in the UK. Methods This was a quantitative study using a survey incorporating a discrete choice experiment (DCE). Respondents were asked a series of questions in which they had to choose between two or more scenarios, differing in terms of attributes. The attributes were: location; familiarity with specialty; culture of the working and learning environment; earnings; working conditions, and opportunities for professional development. The main outcome measure was willingness to accept compensation to forgo a desirable attribute within a training position. Conditional logistic regression models were run separately for males and females. Results A total of 5005 out of 6890 (73%) Foundation Year 2 doctors completed the DCE. The relative value of each attribute was similar for males and females, with location most valued and familiarity with the specialty least valued. There was a pattern of female respondents valuing the move between the best and worst levels of each training attribute more than men, and significantly more than men in respect of the importance of working culture. Conclusions This study adds to existing knowledge in terms of quantifying gendered values in respect of training or residency preferences. That men value a supportive working culture significantly less than women is well established. However, our findings that location, working conditions and working culture are increasingly important to both men and women, suggests that traditional gender norms may be changing. This intelligence can inform gender-responsive workforce planning and innovation, and future research.