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The present study examines racial, ethnic, and gender disparities in career self-efficacy amongst 6077 US citizens and US naturalized graduate and postdoctoral trainees. Respondents from biomedical fields completed surveys administered by the National Institutes of Health Broadening Experiences in Scientific Training (NIH BEST) programs across 17 US institutional sites. Graduate and postdoctoral demographic and survey response data were examined to evaluate the impact of intersectional identities on trainee career self-efficacy. The study hypothesized that race, ethnicity and gender, and the relations between these identities, would impact trainee career self-efficacy. The analysis demonstrated that racial and ethnic group, gender, specific career interests (academic principal investigator vs. other careers), and seniority (junior vs. senior trainee level) were, to various degrees, all associated with trainee career self-efficacy and the effects were consistent across graduate and postdoctoral respondents. Implications for differing levels of self-efficacy are discussed, including factors and events during training that may contribute to (or undermine) career self-efficacy. The importance of mentorship for building research and career self-efficacy of trainees is discussed, especially with respect to those identifying as women and belonging to racial/ethnic populations underrepresented in biomedical sciences. The results underscore the need for change in the biomedical academic research community in order to retain a diverse biomedical workforce.

Background: There has been a groundswell of national support for transparent tracking and dissemination of PhD career outcomes. In 2017, individuals from multiple institutions and professional organizations met to create the Unified Career Outcomes Taxonomy (UCOT 2017), a three-tiered taxonomy to help institutions uniformly classify career outcomes of PhD graduates. Early adopters of UCOT 2017, noted ambiguity in some categories of the career taxonomy, raising questions about its consistent application within and across institutions. Methods: To test and evaluate the consistency of UCOT 2017, we calculated inter-rater reliability across two rounds of iterative refinement of the career taxonomy, classifying over 800 PhD alumni records via nine coders. Results: We identified areas of discordance in the taxonomy, and progressively refined UCOT 2017 and an accompanying Guidance Document to improve inter-rater reliability across all three tiers of the career taxonomy. However, differing interpretations of the classifications, especially for faculty classifications in the third tier, resulted in continued discordance among the coders. We addressed this discordance with clarifying language in the Guidance Document, and proposed the addition of a flag system for identification of the title, rank, and prefix of faculty members. This labeling system provides the additional benefit of highlighting the granularity and the intersectionality of faculty job functions, while maintaining the ability to sort by - and report data on - faculty and postdoctoral trainee roles, as is required by some national and federal reporting guidelines. We provide specific crosswalk guidance for how a user may choose to incorporate our suggestions while maintaining the ability to report in accordance with UCOT 2017. Conclusions: Our findings underscore the importance of detailed guidance documents, coder training, and periodic collaborative review of career outcomes taxonomies as PhD careers evolve in the global workforce. Implications for coder-training and use of novice coders are also discussed.

Background: There has been a groundswell of national support for transparent tracking and dissemination of PhD career outcomes. In 2017, individuals from multiple institutions and professional organizations met to create the Unified Career Outcomes Taxonomy (UCOT 2017), a three-tiered taxonomy to help institutions uniformly classify career outcomes of PhD graduates. Early adopters of UCOT 2017, noted ambiguity in some categories of the career taxonomy, raising questions about its consistent application within and across institutions. Methods: To test and evaluate the consistency of UCOT 2017, we calculated inter-rater reliability across two rounds of iterative refinement of the career taxonomy, classifying over 800 PhD alumni records via nine coders. Results: We identified areas of discordance in the taxonomy, and progressively refined UCOT 2017 and an accompanying Guidance Document to improve inter-rater reliability across all three tiers of the career taxonomy. However, differing interpretations of the classifications, especially for faculty classifications in the third tier, resulted in continued discordance among the coders. We addressed this discordance with clarifying language in the Guidance Document, and proposed the addition of a flag system for identification of the title, rank, and prefix of faculty members. This labeling system provides the additional benefit of highlighting the granularity and the intersectionality of faculty job functions, while maintaining the ability to sort by - and report data on - faculty and postdoctoral trainee roles, as is required by some national and federal reporting guidelines. We provide specific crosswalk guidance for how a user may choose to incorporate our suggestions while maintaining the ability to report in accordance with UCOT 2017. Conclusions: Our findings underscore the importance of detailed guidance documents, coder training, and periodic collaborative review of career outcomes taxonomies as PhD careers evolve in the global workforce. Implications for coder-training and use of novice coders are also discussed.