Concerns about the “endangered physician–scientist” are not new.1–3 Physicians have always found it challenging to juggle the demands of clinical care with the time required to learn research skills and perform investigations. Many physicians have also found it challenging to obtain the protected time that they need to start and maintain a research program,4 to deal with the long time lag between beginning a research career and receiving R01 or similar funding,5 and to cope with the career uncertainties related to the reliance on federal and other grants for salary support.5 For these reasons, many have not been attracted to careers that combine clinical work with research. However, with today's growing focus on the translation of basic science discoveries into clinical practice and the increased need for comparative effectiveness research, the demand for physician–scientists is likely to grow, as is the demand for new ideas and programs to increase the supply of physician–scientists.
In response to the many difficulties that physicians face when they attempt to pursue a research career, the National Institutes of Health (NIH) has established several supportive programs, including the NIH loan repayment program, career development awards, and the Clinical and Translational Science Award.2 Similarly, universities have established various types of career development programs. Although these programs are useful to young investigators,6–8 they do not seem sufficient to substantially increase the cadre of well-trained physician–scientists, nor are they always successful in addressing the problems faced by specific groups of physicians who might be interested in pursuing investigative careers.
Studies indicate that female physicians, for example, are hindered by perceived gender bias,9 a lack of role models in research,10,11 difficulty finding mentors of the same sex,11,12 perceived lack of institutional support,13 and the challenges of raising a family while managing a research program.13,14 For this population, issues related to work–life balance are particularly onerous and include problems related to scheduling meetings that do not conflict with previously planned child care responsibilities,15 a lack of on-site or emergency child care,13 a lack of lactation sites,16 and a lack of promotion paths for part-time work.13,17
Like women, members of underrepresented minority groups report a dearth of role models in research.3,18 Minorities also report discrimination at an institutional level19,20 and insufficient efforts related to retention and promotion.6,17,21 In addition, minorities describe difficulties in dealing with feelings of isolation,19 financial burdens,22 challenges associated with trying to balance a research career with the constant demand for their service on minority committees and their service as mentors for others in minority groups,3 and lower levels of career satisfaction.20
To date, most studies of career development programs have tended to evaluate outcomes related to one or more specific program components. Various studies, for example, have demonstrated that mentoring has a positive impact on the career trajectories of physician–scientists, including those who are women and members of minority groups.23–27 These studies have indicated that effective mentoring is associated with high research productivity,28 increased professional socialization and interactions,27 and greater satisfaction with salaries and promotions.29,30 Moreover, a meta-analysis of results of mentoring studies has indicated that effective mentoring is associated with higher job satisfaction, greater self-esteem, greater organizational commitment, greater perception of promotion opportunities, lower work stress, and lower levels of work–family conflict.31
At the University of Pittsburgh's Institute for Clinical Research Education (ICRE), which offers diverse training and career development programs in clinical and translational science for investigators throughout the pipeline, the career development programs include a mentoring component and numerous other successful training elements. However, we were interested in creating a comprehensive model for career success that would address multiple personal factors (e.g., demographics, education, psychosocial milieu) and organizational factors (e.g., financial resources, infrastructure, training, mentoring) and would also take into account the complex and multifactorial interactions that occur between these personal and organizational factors. In addition, we were interested in creating a model that we could evaluate both in terms of the impact of its individual components and in terms of its overall impact on career success. Here, we describe the steps involved in developing and implementing such a model and establishing methods to evaluate its outcomes.
Our Approach to Developing a Comprehensive Model for Career Success
In 2007, the ICRE recruited faculty members from the various schools of the health sciences at the University of Pittsburgh to form the Research on Careers Workgroup with the purpose of developing a model and metrics for career success. Membership in the workgroup evolved by word of mouth, and anyone who expressed interest was invited to participate. Our initial group included faculty from the schools of medicine, pharmacy, pharmacology, and pediatrics and expanded to public health as well. One of the workgroup's first agenda items was to identify the factors that are related to career success so that we could develop interventions focused on these factors.
Borrowing from the literature of various disciplines, such as business and psychology, we created a comprehensive list of factors that contribute to career success. To better understand these factors, we also informally searched the literature for career-success models. As a group, we reviewed these models to determine the appropriateness of each for physician–scientists. Although we found several models,32–39 we were unable to find one that was comprehensive and flexible enough to be applied successfully to both sexes and to different races and ethnicities. Because we were unable to find a model to fit our needs, over the course of the next two years, we created our own model for career success focused on training physician–scientists. A variety of institutions focused on training physician–scientists can use this model.
Building our own model for career success
In developing our comprehensive model for career success (see Figure 1), we aimed to characterize the associations between career success and the determinants of success at the most basic level. We met monthly as a team to engage in an iterative process of model development. Through an informal process, members of the workgroup searched the literature from various fields, including medicine, business, psychology, and vocational science to inform our model. As a group, we then selected, from this pool, several models with potential relevance to our population of interest: physician–scientists. We broadly examined these models for overlapping and complementary constructs, paying special attention to collapsing or expanding different constructs as they might be relevant to physician–scientists. For example, we decided to combine the constructs of networking and connectedness into networking and expand the construct of satisfaction to job and career satisfaction. Finally, we focused on each resulting construct (e.g., “career success,” “relational factors,” “conflicting demands”), clarifying the meaning and scope of each construct to assess its relationship to the other elements of the model. A more thorough description of this process follows.
Domains of career success.
In reviewing the literature, we found that career success is almost always divided into two domains. These domains may be referred to as objective versus subjective career success,32–39 extrinsic versus intrinsic job success,37,40 or material versus psychological elements of success.35 In each case, the first domain includes concrete or tangible markers of success, such as financial reward and hierarchical status, and the second includes abstract or intangible markers, such as personal and social fulfillment.33,35,38 On the basis of prior theoretical and empirical work, we considered each of these aspects of the broader concept of career success to be highly relevant to physician–scientists.32,41–44
Interestingly, model developers place these two domains of career success in different locations within their models. Judge and colleagues,39 for example, place subjective career success as the ultimate goal in their model, with objective career success as a mediator between determinants and that goal. However, most other model developers position objective and subjective career success as equivalent outcomes.34,45,46 In our comprehensive model for career success, we used the domains of extrinsic career success and intrinsic career success, and we followed the latter approach of viewing these as equally important outcomes. We considered markers of extrinsic success to be financial success, promotion, leadership positions, grants, and publications. We considered markers of intrinsic success to be job satisfaction, career satisfaction, and life satisfaction.
Determinants of career success.
Although our literature review revealed myriad potential determinants of extrinsic and intrinsic career success, these determinants can be broadly classified as related either to the individual (personal factors) or to the institution (organizational factors).
We included the following personal determinants of success in our model—demographics, the psychosocial milieu, education, and personality—which were primarily informed by the literature. For example, first, according to the literature, important demographic factors to include when modeling career success are age, race/ethnicity, gender, socioeconomic status, and family composition,32,43,47–49 so we added these to our model. We also added related but distinct psychosocial factors that may influence career success, including life events (both positive, such as marriage and the birth of a child, and negative, such as divorce and the death of a close family member), burnout, family stress, and care of dependents.32,37,38,50 Several models in the literature emphasized education, training, or other means for preparation of the individual.33,34,43,49 In our model, we sought to emphasize not only the general educational history but also the specific degree or degrees obtained (e.g., MD, PhD, or MD/PhD) and research experience that may not be represented by degree-related work. Finally, examples in the literature universally considered personality factors to be important individual determinants of career success.42,51–55 Many of these studies42,51–55 focused on the qualities that were found and measured by Amabile and colleagues56 in their Work Preference Inventory, including factors such as motivation, challenge, enjoyment, outward orientation, and compensation orientation. In our model, we included motivation, creativity, passion, interest, leadership, self-efficacy, and professionalism.
Various models from the literature identified a number of institutional resources that influence career success.33,34,36,41,43,57 First, in our model, we included important organizational factors, such as the institution's financial resources as well as its infrastructure and overarching support of research. Next, we included training opportunities provided by the institution, such as didactic programs and hands-on research experiences. Then, because models from the literature also emphasized the importance of mentoring and networking,37,54,58–60 we created a category called “relational factors.” Finally, we added a category called “conflicting demands” because studies from the literature frequently referred to the challenge that physician–scientists face in balancing their research with their clinical and service responsibilities.32,38,41,44,57
In Figure 1, we visually present personal factors and organizational factors as equally important, although we recognize that one or the other category may play a more central role with regard to a specific question of interest. We also show a bidirectional arrow between the personal factors and the organizational factors to emphasize that they may also influence each other.
Applying our model
Our conceptual model of career success for physician–scientists provides a highly flexible template for the generation of more concise and testable analytic models. One could develop derivative models based on his or her particular areas of interest or available data. Such models could range from relatively simple to highly complex.
For example, training program leaders might observe that fewer women are promoted at their institutions, as illustrated in Figure 2A. To explore why, they could develop a submodel that introduces one or more potential covariates. The submodel shown in Figure 2B includes two personal factors (female gender and dependent care) plus one career-success factor (promotion) from our comprehensive model. If dependent care for children or elderly parents mediates the gender–promotion association, then (1) gender will be associated with dependent care and dependent care will be associated with promotion, and (2) controlling for dependent care will eliminate the gender–promotion association. The submodel shown in Figure 2C adds an organizational factor (mentoring). With this submodel, training program leaders could determine whether mentoring moderates the effect of dependent care on promotion, and, if so, they could focus on increased mentoring as an intervention.
As another example, illustrated in Figure 3, training program leaders might observe that individuals with more research experience are promoted to leadership positions, and so they would hypothesize that greater self-efficacy acts as a mediator of this relationship. They might further hypothesize that the association of self-efficacy with leadership will be enhanced by an increase in institutional financial support and a decrease in clinical responsibilities. As with the previous submodel, this submodel uses variables from all three key sets of factors in our comprehensive model yet can be modified to address a specific area of investigation.
As a final example, illustrated in Figure 4, investigators might develop a submodel to determine how personality, psychosocial milieu, and extrinsic success factors are linked with career satisfaction. To determine the direction and strength of the associations, this submodel could be evaluated with multivariable analysis methods (e.g., linear or logistic regression).
Testing our model and potential submodels
We have so far outlined hypothetical examples of the multiple types of submodels that could be derived from our comprehensive career-success model. To test these submodels, however, one needs reliable data for the covariates and outcomes of interest. Leaders of training programs will need to think and plan ahead to determine the kinds of data that they need to collect. They will also need the infrastructure to gather the data and a commitment to procedures that ensure the consistent enrolling of physician–scientists and collection of data over time.
Our group, for example, wanted to explore several factors associated with burnout among early-stage physician–scientists. For 179 junior investigators, we obtained socioeconomic and other data collected during a two-year period, and we found that differences in the prevalence of burnout were associated with age, gender, and race/ethnicity but not medical specialty.61 These data represented a good starting point for measuring and observing the appropriate associations but were not sufficient to examine precisely how advanced age, female gender, and minority race/ethnicity contribute to higher rates of burnout among physician–scientists. We continue to gather information by enrolling more junior investigators into our database and also by collecting longitudinal data on all participants. We hope that other program leaders will find this example and our career-success model helpful in testing relationships such as the one that we have described here.
We acknowledge that our work is limited in scope. In particular, the primary aim of this phase of our research was to theoretically explore determinants of career success among physician–scientists. We present several illustrations demonstrating how these theoretical associations may apply to important questions related to career success among this group (Figures 2–4). However, it is important to clarify that these do not represent empiric validations of our model, which we leave for future investigations.
The challenges facing physician–scientists today are substantial. Because clinical training is generally arduous, time consuming, and expensive, many physicians leave the investigative pipeline even before they reach the point of embarking on a research career. Therefore, it is important for academic health centers to have in place the supportive environment, infrastructure, policies, mentoring, and other factors that are needed to maximize the chances of success for individuals who begin investigative careers. Current educators, researchers, and other members of the academic community have the responsibility to gather and analyze data that are relevant to efforts aimed at increasing both the number and success rate of physician–scientists. The model that we have proposed includes factors identified in the literature as important for career success and also serves as a conceptual framework for research into what does and does not work in efforts to develop a positive career trajectory for aspiring physician–scientists.
The authors are grateful to all of the members of the Research on Careers Workgroup, including Terri Collin Dilmore, Susan Meyer, Diana Ploof, and Joan Lakoski, whose intellectual contributions helped with the development of the conceptual model presented here.
This research was supported by grants KL2 RR024154 and UL1 RR024153 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research.
The ideas expressed in this article are solely the views of the authors and do not necessarily represent the official views of the NCRR or NIH.
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