Career Morph: Quantitizing Adversity in Academic Medicine

Negotiating Adversity "I wouldn't discourage people [to go into medicine] based on the amount of adversity that you have to go through" ..." a constant battle," "definitely been a struggle," "it's tough to do," "frustrations," "hard on our family," "I had to be tough," "constantly working," "huge commitment," "med school was brutal," "steep learning curve," "hardscrabble (academic) life," "trickier for women." Introduction Medicine affords high status and compensation in a traditionally male sex-typed occupation, but also requires huge time commitments for training and clinical practice. But how hard is it? How might qualitative research describe differences between stratified groups? In certain qualitative circles there is an explicit denial that we can understand social phenomena through an interpretive lens in causal terms (Crotty, 1998); however, there is support for the inclusion of numerical data in qualitative research practices as a complementary, legitimate and valuable strategy (Becker, 1970; Maxwell, 2010). Numerical descriptive analysis may be a supplemental strategy that is interpretable only within the context of the qualitative component (Morse & Niehaus, 2009). The purpose of this paper is to illustrate how a qualitatively driven (QUAL [right arrow] quan) mixed method design quantitized descriptive data to better illustrate differences between stratified groups. Although women represent almost 50% of current medical students and have comprised more than 30% of medical students for more than 25 years, there is a disproportionate lack of advancement of women physicians into high ranking academic positions (Carnes, 2008; Carr et al., 1998; Carr, Szalacha, Barnett, Caswell, & Inui, 2003; Tesch, Wood, Helwig, & Nattinger, 1995). Research has identified difficulties in work-family and/or work-life balance as a significant cause of the "disproportionately high departure rate" for women; some describing a collision between the ticking biological and tenure clocks (AAMC, 2008; Buddeberg-Fischer, Stamm, Buddeberg, & Klaghofer, 2008; Fox, Schwartz, & Hart, 2006; Howell, Joad, Callahan, Servis, & Bonham, 2009; Jovic, Wallace, & Lemaire, 2006; Leboy, 2009; Shollen, Bland, Finstad, & Taylor, 2009) that supports the "leaking pipeline" theory (Foster et al., 2000; Fried et al., 1996). In a previous study (Analysis 1), we found that women researchers and educators seem to report "more" strategies for multiple role planning and management than women practitioners as a means to facilitate career advancement in academic medicine (Isaac et al., 2013). A review of the text from Analysis 1 revealed that words such as "more," "most," and "all," were terms often used as "quasi statistics" (Becker, 1970) to describe differences between groups. Despite the polarized debate between qualitative and quantitative as well as mixed methods research (Cheek, 2007; Denzin, 2010; Denzin & Giardina, 2006; Morse, 2006), each paradigm "threaten stable identities of others and their own across perspectives and theoretical orientations" (Isaac & Koro-Ljungberg, 2011, p. 247). Perhaps this type of mixed methods research is another "path through the middle" that depolarizes extremes (Deleuze & Guattari, 1987). This "middle" may lead researchers to think differently as transgressive data analysis shifts epistemologies of qualitative research (St. Pierre, 1997). Research suggests that "quantitizing" qualitative data facilitates pattern recognition and improves meaning extraction while verifying interpretations of the data (Sandelowski, Voils, & Knafl, 2009). This QUAL [right arrow] quan design using a praxis lens (Hesse-Biber, 2010) illustrated how quantitizing qualitative data optimized and clarified the analysis rather than only using quasi-statistical terms for stratified data (Morse & Niehaus, 2009). Analysis 1 used a constant comparative methodology that led to a theoretical model (Strauss & Corbin, 1998). …