Career Pathways and Skill Preparedness among Biology PhDs: A Social Cognitive Career Theory Perspective.

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Learn more: PMC Disclaimer | PMC Copyright Notice CBE Life Sci Educ . 2026 Summer 1;25(2):ar25. doi: 10.1187/cbe.25-07-0157 Career Pathways and Skill Preparedness among Biology PhDs: A Social Cognitive Career Theory Perspective Lechen Li a, * , Jue Wu a Editor: Logan Gin Author information Article notes Copyright and License information a School of Human Development and Organizational Studies in Education, College of Education, University of Florida, Gainesville, FL 32611. Author contributions: L.L. conceived and designed the study, analyzed the data and wrote the initial draft of the article; J.W. supervised the study and contributed to the data analysis and the interpretation of the findings; L.L. and J.W. edited and approved the final version of the article. Conflicts of interest: The authors declare no competing financial interest. ✉ *Address correspondence to: Lechen Li ( lechenli@ufl.edu ). Roles Logan Gin : Monitoring Editor Received 2025 Jul 24; Revised 2026 Apr 15; Accepted 2026 Apr 30. © 2026 Li and Wu CBE—Life Sciences Education © 2026 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License ( http://creativecommons.org/licenses/by-nc-sa/3.0 ). “ASCB®” and “The American Society for Cell Biology®” are registered trademarks of The American Society for Cell Biology. PMC Copyright notice PMCID: PMC13229117  PMID: 42207841 Abstract Despite the significant shift in the career landscape for biology doctorates—a decline in academic positions and robust growth in industry employment—postdoctoral training remains the most common immediate next step. Guided by Social Cognitive Career Theory (SCCT), this study examines (1) the self-efficacy, contextual factors, and outcome expectations that shape biology PhD graduates’ early career decisions, and (2) the perceived strengths and gaps in doctoral training identified after entering the job market. Drawing on semistructured interviews with 57 biology PhD graduates from the early career research (ECR) project, we found that graduates who pursued postdocs described strong self-efficacy in research and mentoring, whereas those in non-postdoc roles emphasized confidence in transferable skills. Principal investigators (PIs) were generally supportive yet often lacked the resources or networks to guide nonacademic transitions. Career decisions were also shaped by outcome expectations, with both extrinsic factors and intrinsic motivations influencing career choices. Additionally, regardless of career decisions, nearly all participants reported that their programs effectively developed core scientific and research skills but left critical gaps in business and management skills, advanced statistics and programming, and structured career development. These findings suggest areas in which biology doctoral programs may more intentionally support diverse career pathways, including expanded career development support, interdisciplinary skill-building, and strengthened faculty mentorship for diverse careers. INTRODUCTION Doctoral education has been expanding dramatically in recent decades ( Sarrico, 2022 ), with biological and biomedical sciences undergoing some of the most robust growth. In the United States, the number of PhD graduates in biology 1 grew from 8782 in 2018 to 9575 in 2023 ( National Center for Science and Engineering Statistics [NCSES], 2024 ). This growth, however, has occurred alongside a significant shift in the career landscape for these graduates. Recent employment data from the Survey of Earned Doctorates (2018–2023) reveal two countervailing trends for biology doctorates: a significant decline in academic positions (30.9% to 22.1%, representing one of the steepest drops among STEM fields) alongside robust growth in industry employment (49.1% to 61.6%; NCSES, 2024 ). These contrasting trends present a critical challenge for doctoral programs to ensure their training aligns with the reality of today's diverse career paths ( Didiano et al ., 2019 ; Wood et al ., 2020 ) as well as economic and societal needs ( Enders, 2005 ). Despite experiencing some of the most evident shifts in career opportunities, postdoctoral 2 training, which has traditionally been framed as specialized and essential preparation for research-intensive faculty careers ( Melin and Janson, 2006 ), remains the most common immediate next step for biology doctorates. Around 60% of biology students report definite postdoctoral plans, which is the highest percentage among all majors ( NCSES, 2024 ). As the number of tenured and full-time faculty positions plateaus or even shrinks ( Diehl, 2021 ; Roy et al ., 2024 ), many postdoctoral appointments have grown longer in duration yet remained poorly compensated ( Athanasiadou et al ., 2018 ). Those pursuing academic or research careers often end up doing multiple postdoc terms and staying in these positions for years, or ultimately transition to nonacademic careers ( Powell, 2015 ). While some scholars argue that postdocs provide valuable opportunities for skill development and career preparation ( Fuhrmann, 2016 ; Yadav and Seals, 2019 ), others view them as an inefficient and often exploitative extension of graduate training ( Stephan, 2013 ; Johnson, 2018 ). These patterns reveal a more complex landscape in which postdoctoral appointments are variously understood as default extensions of graduate school, necessary credentials for academic careers, and meaningful spaces for skill-building and exploration, even for those who eventually move outside academia ( Gibbs et al ., 2015 ; Lu et al ., 2023 ). Previous work has documented how structural forces, such as labor market conditions ( Auriol et al ., 2013 ), institutional pressures ( Waaijer et al ., 2018 ), and macroeconomic trends ( Beltramo et al ., 2001 ), have contributed to broader shifts in PhD employment and the expansion of non-academic career opportunities. Literature has also highlighted persistent skill gaps between doctoral training and employer needs ( Mowbray and Hasle, 2010 ; De Grande et al ., 2011 ; Ferrie and Scott, 2021 ). However, we know far less about how graduates themselves—those who have directly navigated the transition—interpret the relationship between their training and the careers they enter. Our current study addresses this gap by centering the retrospective accounts of biology PhD graduates who have recently entered the job market. Much of the existing literature (e.g., Thiry et al ., 2015 ; Seo and Yeo, 2020 ) has focused on how current PhD students anticipate and navigate possible futures during training. By contrast, we focus on graduates who are already in postdoctoral, academic, and nonacademic roles and examine how they look back on their doctoral programs. Guided by the Social Cognitive Career Theory (SCCT; Lent et al ., 1994 ), this study addresses the following research questions: 1) What perceptions of self-efficacy, contextual factors, and outcome expectations do biology PhD graduates describe as shaping their career decisions? 2) What perceived skill strengths and gaps in doctoral training do these graduates identify in relation to their early career outcomes? These questions are particularly relevant for those who design and deliver biology graduate programs, mentor PhD students, and make decisions related to curricula, advising structures, and program-level training priorities ( Wolyniak, 2003 ; Clement et al. , 2020 ). The first research question clarifies the factors that biology PhD graduates perceive as shaping their early career paths, while the second highlights specific areas where curricula and mentoring practices might be strengthened to better support the diverse careers biology PhDs pursue. By centering biology PhD graduates’ accounts of their training, perceived skills, and early career decisions, this study provides evidence to inform ongoing efforts to improve graduate biology education by structuring training to better align with contemporary demand and to prepare the next generation of scientists for a wide array of successful and fulfilling careers ( Hernandez et al ., 2024 ). LITERATURE REVIEW The significant shift in PhD graduates’ career outcomes—marked by a decline in academic positions and substantial growth in industry employment—has been well-documented in the literature (e.g., Cyranoski et al ., 2011 ; Sauermann and Roach, 2016 ; Zimmerman, 2018 ). Over the past two decades, employment in industry or business has become a major destination for PhD graduates in the United States across most STEM fields ( NCSES, 2024 ), which is often due to the oversupply of PhDs relative to tenure-track positions ( Cyranoski et al ., 2011 ; Larson et al ., 2014 ) and industry's increasing demand for advanced, doctoral-level research skills ( Kemp et al ., 2012 ; Zimmerman, 2018 ). A growing body of research has begun to explore biology PhD career trajectories from students’ perspectives. Using a longitudinal survey, Roach and Sauermann (2017) found that more than 40% of biology PhD students reported a decline of interest in the academic career path—the highest rate among all fields. Additionally, Sinche et al . (2017) found that PhD graduates often reported comparable levels of job satisfaction, regardless of working in academia or industry, challenging the notion that non-academic paths represent “second-best” alternatives ( Purcell et al ., 2006 ). Biology sits squarely within these broader shifts. On the one hand, biology PhDs enjoy a diverse set of potential career routes in academia, industry, government, and other sectors ( Lu et al ., 2023 ). On the other hand, patterns of training and hiring have not always kept pace with this diversification ( Sauermann and Roach, 2012 ). In biology, postdoctoral training remains particularly prevalent, even as long-term academic opportunities are constrained ( Denton et al ., 2022 ). This makes biology a useful context for examining how graduates understand and navigate early career options in a changing employment landscape. The Postdoc System This transformation has raised growing concerns about the effectiveness of the postdoc system, and whether it remains a viable training model in an era of diversifying career outcomes (National Academy of Science, 2014 ; Garrison et al ., 2016 ; Grinstein and Treister, 2018 ). Postdocs were traditionally viewed as a “temporary appointment with a primary purpose of providing additional research or scholarly training for an academic or research career” ( Association of American Universities, 1998 ). As outlined in the introduction, however, postdocs now occupy a contested and increasingly complex role in the contemporary scientific workforce, particularly in biology. Rather than solely bridging to tenure-track faculty positions, postdocs now operate within a landscape of constrained academic hiring, diversified career outcomes, and ongoing debate about their value and equity. Some empirical studies have examined how PhD students and early-career researchers themselves understand postdoctoral training. A large-scale survey by Sauermann and Roach (2016) , for example, revealed that while students become more open to non-academic paths over time, many still describe the postdoc as the “default” next step. In this context, postdocs may function as a safe extension of familiar academic training, particularly when academic positions are scarce and alternative careers feel less clearly mapped or supported ( Lin and Chiu, 2016 ). Qualitative work by McAlpine and Emmioglu (2015) added nuance to this pattern by showing that PhD students and post-PhD researchers make career decisions in relation to changing opportunity structures and future possibilities, including personal relationships, scholarly communities, and geographic constraints. These studies suggest that postdoctoral positions are not only institutionalized in hiring structures but also deeply normalized in students’ expectations about what a successful trajectory should look like. Skills, Training, and Preparation in Doctoral Education Alongside shifts in employment patterns, related scholarships have examined how well doctoral programs prepare graduates for the skills needed across different sectors. For example, Fuhrmann (2016) emphasizes that postdocs can serve as critical sites for advanced research training and structured professional development, particularly when programs integrate explicit career planning, mentoring, and skills training into the postdoctoral experience. This echoes with Yadav and Seals’ ( 2019 ) findings of the positive effect of professional development workshops on postdocs’ career-related skills, such as academic job application. On the other hand, Johnson (2018) and Stephan (2013) similarly argue that some postdocs have become a relatively low-cost, flexible labor force that extends the precarious conditions of graduate training with limited additional career benefits. Despite growing interest in nonacademic careers, many biology PhD programs continue to emphasize academic preparation. Multiple studies (e.g., Mowbray and Hasle, 2010 ; De Grande et al ., 2011 ; Ferrie and Scott, 2021 ) have found that while PhD programs excel in developing scientific knowledge and research skills, they often fall short in training PhD students with skills that are valued by employers, especially in industry settings. De Grande et al . (2011) , for example, found a clear mismatch between the skills prioritized by doctoral candidates and those sought by employers: among 27 skills assessed, technical skills were ranked highest by employers but only 14th by doctoral candidates, while research skills topped the candidates’ list but were only sixth for employers. This disconnection could lead to insufficient preparation and support for students considering the industry options, despite the fact that these are increasingly common choices among STEM PhD graduates ( Turk-Bicakci et al ., 2014 ). Yet existing studies leave two related gaps. First, most existing studies (e.g., Cyranoski et al ., 2011 ; Roach and Sauermann, 2017 ) rely either on macro-level workforce data or on the perspectives of current PhD students who are still anticipating possible futures. Second, although previous research like Nersesian et al . (2019) and MacMillan et al . (2023) has raised important questions about doctoral preparation and career readiness, it tells us less about how graduates themselves, after entering professional roles, retrospectively evaluate their preparation in light of concrete job demands. As a result, we know relatively little about how biology PhD graduates themselves interpret postdocs and other early-career positions in their trajectories, how they connect those roles to the skills and supports they received in their programs, and where they identify strengths and gaps in their training. The present study addresses this gap by drawing on Social Cognitive Career Theory (SCCT) to analyze in-depth interviews with biology PhD graduates from a range of early-career positions. While a small number of studies (e.g., St. Clair et al ., 2017 ; Butz et al ., 2018 ) apply SCCT to examine career development and graduate training, they primarily focus on program design and trainee development, instead of on graduates’ own evaluations of how doctoral training aligned with actual early-career roles. In this study, we centered on PhD graduates’ retrospective accounts of self-efficacy, contextual supports and barriers, and outcome expectations to examine how doctoral training aligns with the careers biology PhDs pursue and to identify implications for curricula and mentoring structures. For biology education researchers and practitioners, these studies highlight that doctoral education is situated within a labor market characterized by increasingly diverse career pathways, the continued normalization of postdoc training, and persistent mismatches between the skills doctoral programs emphasize and those valued by employers. More importantly, they raise practical questions about which skills and forms of preparation are explicitly targeted in the curriculum and how programs support students’ preparation for roles beyond academia. Theoretical Framework: Social Cognitive Career Theory (SCCT) Social Cognitive Career Theory (SCCT; Lent et al. , 1994 ) is a conceptual framework that explains how individuals develop career interests, make career choices, and achieve career success. It suggests that personal background and traits influence individuals’ perceptions of career success and expected outcomes. These perceptions and beliefs are shaped by academic and professional experiences over time. SCCT identifies three core constructs that shape career-related behavior: self-efficacy, contextual influences, and outcome expectations. In the context of PhD students’ career development, SCCT provides a useful lens for understanding the interplay between personal, behavioral, and environmental factors in shaping career development (e.g., Bishop and Bieschke, 1998 ; Wang et al ., 2007 ; Seo et al ., 2021 ). Self-efficacy refers to one's belief in one's ability to successfully perform tasks or achieve goals ( Bandura and Wessels, 1997 ). In both STEM and non-STEM fields, PhD students with high self-efficacy in research, mentoring, or other academic and professional skills are more likely to pursue careers that align with these strengths ( Connolly et al ., 2018 ; Cordova, 2022 ). Conversely, low self-efficacy in certain areas may deter students from pursuing careers that demand those skills. Contextual factors also play a critical role in shaping career development ( Bakken et al ., 2006 ), where students’ expectations, self-efficacy, and intentions are shaped by institutional environments. Previous studies have examined PhD student career outcomes as a function of faculty mentorship ( Dewett et al ., 2005 ), institutional support ( Kim and Karau, 2009 ), interactions ( McGaskey, 2015 ), and early career training ( Austin, 2002 ). Lastly, outcome expectations refer to the anticipated consequences, both short-term and long-term, of pursuing specific career paths ( Young et al ., 2020 ). The consequences typically include potential benefits (e.g., job satisfaction, stability, salary) and challenges (e.g., job market competitiveness, stress, work-life balance). For example, a PhD student may choose an industry career over academia if they perceive better job security or higher salaries in the former ( Woolston, 2021 ), or they consider the stress and competitiveness in academia unpleasant ( Waaijer et al ., 2018 ). In this study, these outcome expectations also capture both intrinsically valued aspects of work and more extrinsic considerations ( Ryan and Deci, 2000 ). Some graduates may be drawn to careers for the inherent satisfaction they expect from research, mentoring, ethical alignment, or intellectual autonomy, while others weigh more instrumental outcomes such as salary, security, and lifestyle. This framing is also consistent with Schunk's ( 1995 ) argument that self-efficacy shapes choice, effort, and persistence, which helps explain why similar career outcomes may be interpreted differently depending on whether graduates feel capable of succeeding in the roles they are considering. Figure 1 summarizes how we applied SCCT to guide our analytic framework. In this model, doctoral training experiences (e.g., coursework, lab work, program structures) shape graduates’ self-efficacy, contextual factors, and their outcome expectations. Specifically, self-efficacy refers to graduates’ perceived capability in domains salient to their early work, such as conducting independent research and contributing technical expertise. Contextual factors refer to the environment that supports or constrains career pursuits, such as PI guidance. Outcome expectations refer to the anticipated benefits and tradeoffs associated with different career paths, such as intellectual fulfillment, autonomy, salary, and work-life balance. Together, these components shape graduates’ early career trajectories. FIGURE 1. Open in a new tab SCCT-informed model of biology PhD graduates’ early career choices. Graduates’ reflections on the strengths and gaps of their doctoral preparation are placed alongside: as they look back from their first post-PhD positions, they reassess how well their programs prepared them, and identify uneven alignment between doctoral training and the early career trajectories. We also include a dotted arrow from “Reflections on doctoral training” back to “Doctoral training experiences” to indicate the implications of our findings, which are central to our second research question and inform future discussions about how doctoral preparation aligns with diverse career pathways. METHODS The present study employed a qualitative approach to analyze de-identified secondary interview data from the Trajectories in Early Career Research (ECR) project from Feldon et al . (2023) , available via the Open Science Framework ( https://osf.io/hymus/ ). Study Context The ECR project is a mixed-method, longitudinal study that tracks the developmental trajectories of 336 biology PhD students 3 . These students, recruited from 53 research-intensive universities (approximately 80% classified as R1 institutions), began their PhD studies in Fall 2014. The longitudinal dataset covers various aspects of doctoral education, including students’ demographics, degree progress, program quality, campus climate, publication activity, graduate advising, career outcomes, networks, and more. Participants completed annual surveys at the end of each year to reflect on their experiences and developments, with approximately 90 participants interviewed each year. For the first four years, interview questions predominantly focused on students’ development within their doctoral programs, such as their academic accomplishments, socialization, and self-efficacy. From year 5 to year 7, interview questions focus more on students’ career trajectories, including their employment situations, types of positions, and networks. A growing body of research has drawn on the early career researcher (ECR) dataset to examine biology PhD students’ developmental trajectories using both quantitative and qualitative approaches. These studies have documented patterns in research skill development ( McCain and Roksa, 2023 ), advising and mentoring practices, ( Blaney et al ., 2020 ; Griffin et al ., 2023 ), socialization ( Jeong et al ., 2020 ; Li and Wu, 2025 ), and issues around equity and diversity (e.g., Roksa et al ., 2018 ; Wofford et al ., 2021 ; Roksa et al ., 2022 ). A few studies examine students’ career choices and trajectories, but have given less attention to how graduates themselves, looking back from their first post-PhD positions, make sense of their skills, supports, and career decisions across both academic and non-academic paths. For instance, Blaney et al . (2022) draw on the first four years of interviews with biological science PhD students to examine how they perceived their academic and professional trajectories over time. They found wide variation in how students described their trajectories, ranging from relatively linear and stable interests to highly turbulent, nonlinear pathways, which were largely shaped by student–advisor interactions, scientific identity, and students’ previous privilege. Another study ( Griffin et al ., 2023 ) focuses on career decision-making during the PhD by analyzing how biology PhD students exercised agency in the face of career indecision. They identify a distinction between “uncommitted” students, who actively explored options and framed indecision as developmental, and “uncertain” students, who reported more fear, less perceived control, and fewer strategies for moving toward career goals. Analytical Approach and Sample Despite the rich, longitudinal quantitative data, we adopted a qualitative approach for several reasons. First, our research questions focus on how graduates in early career positions reflect on their doctoral training and career choices. These questions are less about estimating the prevalence of particular outcomes across the full ECR sample and more about understanding the meanings graduates assign to their experiences, making in-depth interviews an appropriate methodological approach. Although the survey provides a larger sample size, its fixed-response items were not designed to capture the nuanced narratives that are central to this study. In contrast, the interviews invited participants to describe their trajectories in their own words, elaborate on why certain options felt feasible or attractive, and reflect on the alignment between their training and the everyday demands of their current positions in greater detail. The analytic sample includes a specific subset of interview participants—those who graduated and began working during the year they were interviewed. To identify these participants, we used the survey data to obtain the IDs of students who graduated during the period from year 5 to year 8. These IDs were then cross-referenced with the interview data to determine which interview transcripts belonged to students who graduated and worked at that time. In total, 57 students were selected in the analytical sample: 12 students graduated and worked in year 5, 23 students graduated and worked in year 6, and 22 students graduated and worked in year 7. Table 1 presents a summary of participants’ profiles, including their employment and perceived preparedness of skills. TABLE 1. Summary of participant profiles and perceived skill areas ( n = 57) Participant ID Time to degree (years) Employment Perceived skill strengths Perceived skill gaps 4 6 Scientist at a biotech company Technical skills, training others, equipment management How different institutions operate 8 7 Postdoc Teaching, research Patience 9 5 Postdoc Programming, biological research analysis Consulting, management, finances 11 5 Consultant Scientific skills, independence Business, data analysis 12 6 Consultant Scientific techniques Coding, statistics 13 7 Postdoc Independent, scientific experiment, technical skills Career, employment opportunities 15 6 Science policy work at government Research, writing, presentations Not explicitly mentioned 18 7 University research secretary Technical skills, teaching, communication Positive mindset 25 7 Postdoc Science, project management Not explicitly mentioned 28 6 Postdoc Scientific techniques Mentorship 33 6 Scientist roles at a synthetic biology company Scientific techniques, data analysis, coding Industry job application 51 5 Postdoc Research, collaboration Developing new skills (general), social skills 57 5 Microscopy specialist (industry) Scientific techniques/skills, writing Industry career knowledge, resume, finance, network 59 6 Postdoc Research, data analysis, science communication More data analysis, coding 60 6 Cell biology department head at a small biotech company Scientific techniques, research, time management Network, more technical skills 68 7 Postdoc Teaching, research, statistical analysis Explore outside of familiar area, research across disciplines 71 7 Postdoc Independence, mentoring, research Statistics, programming 72 5 Postdoc Research and development, writing Interpersonal, statistic 79 5 Unemployed (previous lab operations associate) Management, teaching Industry experience, analytical compositions/testing 88 5 Preceptor Teaching, scientific research Statistics, writing 97 6 Postdoc Time management, research writing More writing (not explicitly mentioned 101 7 Postdoc Science, research, teaching Bigger picture of science 104 6 Private school teacher Planning, teaching Job application 120 7 Postdoc Technical skills, mentoring Time management 123 5 Postdoc Research, technical skills, scientific writing Business, interview training, clinical research 137 7 Small biotech startup Research, reading Network, business 144 6 Academic editor Presentation, task-based work Career alternatives (especially option besides postdoc) 145 7 Postdoc Lab work, research design Imaging technique, communication 150 7 Postdoc Research, mentoring Fellowship application 151 6 Unemployed Scientific techniques, research Communication, grant information 157 6 Postdoc Technical skills, publication, collaboration Wider breadth of research 163 6 Postdoc Writing, research analysis, data analyzing Some specific lab techniques (Not explicitly mentioned) 173 7 Postdoc Critical thinking, mentoring Writing 177 6 Postdoc Critical thinking, mentoring, presentation Research thinking, creativity 178 5 Postdoc Geneticist, research Molecular work, bioinformatics 181 6 Not looking for job (had a newborn) Lab work, project management, scientific experiment Managerial roles 187 5 Consultant Scientific techniques, data analysis, research Interview skills, management 199 6 Pharmaceutical sales rep Data analysis, teamwork Not explicitly mentioned 206 7 Product development at biotech company Research, experiment, teamwork Work-life balance, communication 209 7 Postdoc Research, experiment, computational analysis skills Applying skills 214 7 Postdoc Technical skills, research, experiment Job search advice, career development 219 7 Postdoc Scientific skills, technical skills, lab work Career development 228 7 Lab technician Time management, research Not explicitly mentioned 249 7 University bioinformatics analyst Presentation, science, bioinformatics Computer science skills 252 7 Postdoc Mentoring, network Training in conservation biology, writing 257 6 Biotech startup Research, data analysis Machine learning, coding, non-academia culture 258 5 Clinical microbiology fellow Scientific publication, writing, presenting Business, communication, service training 265 5 Biochemist at Government organization Collaboration, networking, scientific techniques Statistic, modeling 279 6 Postdoc Scientific techniques, mentoring, research Writing skills, organization 295 6 Postdoc Scientific techniques, independence Business, grant application 297 7 Legal field firm Communication, writing Statistics, data analysis software 299 7 Science writing internship Editing, independence Time management, career development 300 6 Postdoc Data analysis, presentation, scientific techniques Statistics, machine learning, computer 302 7 Unemployed Data analysis, scientific writing, mentoring Business, clinical training 308 6 Postdoc Technical skills, data analysis, writing Not explicitly mentioned 310 6 Starting a Master of Commerce Data analysis, planning, science Business, networking, internship 319 6 Postdoc Collaboration, research writing Not explicitly mentioned Open in a new tab Interview Questions Guided by the social cognitive career theory ( Lent et al ., 2002 ), we focused on interview questions related to students’ self-efficacy, contextual factors, and outcome expectations, and how these characteristics influence their career outcomes.  For example, questions such as “What made you choose this option?” were used to assess participants’ confidence in their roles and career decision-making (self-efficacy), while others explored external influences like mentorship and institutional support (contextual factors), or participants’ expectations and reflections on their career paths (outcome expectations). See Appendix I for the full list of interview questions selected and detailed rationales. Data Analysis The qualitative data analysis was led by the first author and followed Saldaña's ( 2021 ) approach of performing multiple coding cycles. The first cycle was inductive and open-ended, employing In Vivo coding. The first author reviewed each transcript to identify key codes and themes expressed by the participants. This first stage of coding focused on identifying patterns and concepts directly from the interview transcripts without imposing a pre-determined framework. Example codes include: “employment” (child codes: “postdocs”, “consultant”, “scientist in industry”), “skills” (child codes: “critical thinking”, “time management”, “technical expertise”), “advisors” (child codes: “hands-off”, “supportive”, “career recommendation”, “lack of academic-insight”, “network”), “work environment” (child codes: “work-life balance”, “isolated”, “immature”). Throughout open coding, the first author developed an initial codebook that was iteratively refined through regular meetings with the second author. During these meetings, the second author reviewed code definitions, examined selected coded excerpts, and provided feedback on code application and thematic interpretation. Discrepancies in interpretation were discussed and resolved through consensus, resulting in refinement of code definitions and improved consistency in coding. In the second cycle, the first author conducted deductive coding by grouping initial codes into larger thematic categories based on our SCCT-informed analytical model ( Figure 1 ). These included self-efficacy (e.g., perceived skills strength and weakness), contextual factors (e.g, advisor influence, institutional support), and outcome expectations (e.g., intrinsic and extrinsic expectations). For example, initial codes like “technical expertise,” “critical thinking,” “mentoring” were grouped under “perceived skill strength,” while codes like “salary,” “work-life balance,” “burnout” were grouped under “extrinsic expectations.” In these cycles, reported skills and areas of preparation emerged inductively from participants’ narratives and were then interpreted deductively through the SCCT framework as evidence of self-efficacy, contextual influences, and outcome expectations, which allowed for an examination of how participants’ experiences aligned or diverged from SCCT. The second author again reviewed the thematic structure and provided feedback to ensure alignment between the data and the analytic framework. A summary of themes, definitions, and representative quotes is presented in Appendix II. Throughout this process, MaxQDA qualitative data analysis software was utilized to organize the interview transcripts, apply the in vivo and deductive codes, and assist in identifying emerging themes and patterns. Positionality We acknowledge that our position experiences shape the ways we interpret participant narratives and represent the findings. The first author is a PhD student whose research focuses on graduate education in higher education. His own experience navigating the transition from PhD training to career planning informs a deep interest and sensitivity in how graduates make sense of their options and outcomes. The second author is an assistant professor of higher education with expertise in graduate education and STEM fields. Her perspective as a researcher and doctoral advisor offers valuable insight into how institutional structures and training practices influence post-PhD career trajectories. While our individual backgrounds bring different lenses to the study, the use of secondary data allows us to analyze participants’ perspectives separately from the original data collection process, minimizing potential researcher influence. We also engaged in collaborative coding and peer debriefing to ensure consistency and strengthen the credibility of our interpretations. FINDINGS Before addressing our research questions, we first provide an overview of participants’ immediate post-PhD career outcomes. Table 1 summarizes the 57 participants in our analytic sample, including their IDs, time to degree, demographic characteristics, immediate post-PhD employment, and the skill areas they identified as strengths and gaps in their doctoral training. As shown in the table, participants entered a wide range of roles, reflecting the broader trend of STEM PhD graduates moving into diverse career pathways ( Garrison et al ., 2016 ; NCSES, 2024 ). Despite this diversity, postdocs were unsurprisingly the most common immediate career step, consistent with previous reports on the predominance of postdoctoral training ( Arbeit and Yamaner, 2021 ). Specifically in our analytical sample, participants transitioned into a range of positions, including postdocs (30/57, ∼53%), industry roles (13/57, ∼23%; e.g., biotechnology, consulting, pharmaceuticals), policy and clinical fellowships (2/57, ∼4%), nonpostdoc academic appointments (6/57, ∼11%), and other alternatives or experienced periods of unemployment or transition (6/57, ∼10%; refer to Table 1 for detailed participant profiles). In the following sections, we organize our findings around our two research questions based on the themes that emerged and the core components of SCCT, and we use the career distribution as a backdrop for our SCCT-informed analysis. For RQ1, we examine how self-efficacy, contextual factors, and outcome expectations relate to graduates’ decisions to pursue various early career roles. For RQ2, we return to the patterns of perceived strengths and gaps shown in Table 1 to present graduates’ reflections on the strengths and gaps of their doctoral training, including the skills they felt most prepared for and the areas they saw as underdeveloped or wished had been more strongly supported. Research Questions 1: What Perceptions of Self-Efficacy, Contextual Factors, and Outcome Expectations do Biology PhD Graduates Describe as Shaping their Career Decisions? Self-efficacy: Domain-specific. To examine the first component, self-efficacy of RQ1, we looked at participants’ reported skills/abilities across trajectories. Our findings demonstrate that while almost all biology PhD graduates reported high self-efficacy in scientific skills (e.g., critical reading and analysis), distinct patterns emerged based on the specific career trajectory chosen. Self -efficacy in research and mentoring across postdocs . Among postdocs, self-efficacy was concentrated in core research activities, growing independence as a scientist, and, for many, in mentoring. Participants described themselves as able to design and troubleshoot studies, think critically about research questions, and manage complex projects with relatively little supervision. Participant 177 reflected on this shift in her capabilities: My ability to think critically… to figure out how to propose experiments, assess whether or not the proposed experiments actually answer the question… those are skills that I need now that I largely developed in graduate school… reading literature and integrating things that I've read and seen and whatnot, trying to propose something knew out of that is something that I worked on in graduate school that I need now. In addition, the postdoc position was often described as an essential step for consolidating a sense of independence and sustaining an academic trajectory that participants already felt capable of pursuing. Participant 71 defined her professional confidence in terms of autonomy over her project: The fact that I have so much autonomy over my project really, for me, is the defining thing between feeling like a postdoc and feeling like a graduate student… It's very gratifying to be able to present, to [my mentor] a bunch of data and then tell her, “Well, I think we should do these three things and maybe in this priority.” For the most part, she says, “Yeah, go for it.” For her, being trusted to propose and prioritize next steps affirmed her ability to work as an independent scientist, and this sense of self-efficacy in research independence contributed to her decision to pursue a postdoc rather than leaving research or moving immediately into a non-research role. The postdoc position offered a context in which she could keep doing work she already believed she could do well, while moving closer to the level of responsibility she associated with a faculty position. Other postdocs described a similar confidence in carrying their established ways of doing science into new labs and projects. Participant 308 noted that, “I'm able to bring many different skills and strategies that I learned previously to the new lab… although what I'm doing is slightly different, how I think it, the way that I do it, is similar.” In his narrative, self-efficacy was developed on a stable sense of himself as someone who knows how to think through experiments, solve problems, and contribute intellectually in a research setting, rather than a specific technique. In SCCT terms, the confidence of being able to operate autonomously within research-intensive environments makes postdocs appear both feasible and attractive, and helps explain why many graduates who feel efficacious as independent scientists choose to remain in academia as postdocs. For those deliberately seeking to apply and expand their technical repertoire, self-efficacy was tied to taking on new, specialized methods. Participant 209 described using familiar experimental techniques in the new environment: I've been really focused on (what) I'm doing, rather than learning new experimental techniques or that sort of thing, because I feel like I learned a lot of those before grad school and during grad school, and now I'm really focused on using those experimental techniques that I'm really comfortable with and sort of generating a huge dataset and then learning expanding computational techniques for analyzing those data. Here, postdoctoral training is framed as a context in which existing strengths support the acquisition of more complex skills; his belief that he can “learn expanding computational techniques” is central to remaining competitive in a research environment. Together, these accounts show that for many graduates in postdoctoral roles, self-efficacy was anchored in their ability to design, execute, and increasingly direct research, as well as to extend their expertise into new methodological domains. Several participants identified mentoring as something they both enjoyed and did well transition into postdoc roles that allowed them to continue working closely with students and junior trainees. Participant 279 described her mentoring experience during her doctoral program, “I've had a number of undergraduates I have mentored. For me, when I mentor them, maybe I just have more energy for this…I like mentoring the student in terms of, yeah, their career goals and work in the lab.” Similarly, Participant 71 discussed her role as a mentor for many younger students: Many people, probably, would consider me a mentor for some of the younger graduate students in the lab, particularly because my boss was on sabbatical for one of the years in graduate school…I would say a lot of what I provided was technical recommendations and suggestions, how to troubleshoot something or different ways to, maybe, approach setting up an experiment. I think some of it may have been on the side of cheerleading…I think that's at least a component of my personality that makes it easy for people to look to me for reassurance. These narratives suggest that graduates did not experience mentoring as generic, secondary tasks, but as domains in which they felt especially energized and capable. Self- efficacy in applying scientific skills across industry ro les . For graduates transitioning directly into industry, biotech, or consulting roles, self-efficacy is centered less on disciplinary depth and more on the utility of their transferable skills in fast-paced, applied settings. Participants emphasized confidence in problem-solving, navigating large and complex datasets, and making research actionable for teams and clients. Participant 187, a management consultant, emphasized how PhD training conferred a competitive advantage in handling scientific complexity: A lot of the skills that you have as a PhD are really easily transferable—where problem solving; being able to look at large, complex data sets to figure out what's important really fast. I can read scientific literature really quickly, because I've been doing it for years now. It's just that I can go in with the knowledge already and say—this whole intro paragraph I could skip. I can look at figure four. That's all that matters to me; moving on to the next paper. Stuff like that saves time and it's something that—a firm that works to my scientists needs. Here, his sense of efficacy lay in being able to rapidly filter and synthesize information in ways that are directly valuable to his firm. That belief helped make consulting feel like a viable and attractive next step rather than a departure from his training. On the other hand, Participant 4, a scientist at a biotech company, described how his role quickly centered on owning a new microscopy platform: My role really was, as I started there, we received our first microscope for that technology, and so I have been the main driver of getting that up and running at our site and being fully operational, so I've become our site expert in that technology. For Participant 4, self-efficacy is not just about knowing how the instrument works, but about being the person who can take a complex tool from delivery to “fully operational” and serve as the expert others depend on. That sense of being able to assume technical leadership within a team made the industry role feel like an extension of his doctoral training, a role he expected to keep doing what he felt he did well while contributing directly to his company's goals. Other graduates described their efficacious skills that would serve them well in team-based environments. Participant 33, a scientist at a synthetic biology company, pointed to both scientific and interpersonal self-efficacy: My knowledge of those metabolic pathways, I think, will be very valuable coming into this, for sure. I think other hard skills… working with coding, doing different data processing platforms, that'll be really valuable, I think, especially in the industry setting… In industry particularly, you're working in teams… I think the interpersonal skills of just being able to work in a team and work with other people, I think, will come in very handy… Something I have honed in academia is being able to use other people's expertise… I think in industry, that'll be very valuable as well. For participants like him, industry felt like a good fit not simply because it offered different jobs, but because they believed they could successfully combine scientific knowledge, quantitative skills, and collaboration to contribute in those roles. Consistent with SCCT, these patterns suggest that graduates did not simply choose postdoc or industry tracks; instead, they gravitated toward roles that matched the domains in which they felt most capable and energized, such as conducting/directing research and mentoring students as postdocs, or applying scientific and analytical skills to solve concrete problems in team-based industry settings. Across sectors, their career choices reflected domain-specific self-efficacy, guiding them toward roles in which they believed they could contribute most effectively. Contextual factor: PI support is well-intentioned but limited. To address the second component of RQ1, we examined the contextual factors that influenced PhD graduates’ career outcomes. A salient contextual factor influencing participants’ career development was the role of the principal investigator (PI) as a mentor. While most participants described their PIs as broadly supportive of their goals, many also noted a lack of informed guidance, resources, or professional networks to aid in transitions beyond academia. This disconnect was particularly pronounced for those pursuing careers in nonacademic sectors. For instance, participant 157 reflected on his experience with his advisor during the job search, “He doesn't know much about the industry side of this field, but certainly for academia, I think he was a great mentor for preparing for the next step of a career as a postdoc.” Participant 199, working in pharmaceutical sales, echoed this pattern of strong support within the academic track but limited relevance outside it: “With the career that I'm going into (industry), he doesn't have many contacts specifically in that field… he hasn't been able to do that [provide direct support].” Others described their PIs as unaware of the structure and expectations of nonacademic job markets. Participant 11, who pursued a path into consulting, said, “I think that was kind of a unique situation in that I was going in a path that he hadn't had anybody follow before… not all that many PIs have.” In some cases, the perceived support from PIs was not followed by actionable help. Participant 187 discussed, “It's one of those things where people say that they're open to non-academic tracks… but it doesn't come across when it actually comes to it.” This disconnect led to frustration, particularly among participants who felt they had to navigate alternative paths on their own. Participant 302, who was unemployed at the time, described his experience, “I figured out my own career path. I did it on my own. There was zero help from her. I got nothing but walls placed in front from her in terms of getting the actual job position.” While most PIs were not viewed as unsupportive, their academic orientation and limited experience with nontraditional careers often left students without the proper mentorship, information, or networks necessary to pursue nonacademic roles. As Participant 214, a postdoc, summarized, “There wasn't a lot of guidance, or advice, or anything like that based on career recommendations.” Outcome expectations on jobs : Extrinsic v ersu s i ntrinsic . To address the third component of RQ1, we examined how graduates evaluate potential career paths through outcome expectations. Participants in this study explicitly reported that their outcome expectations significantly influenced their career choices. These expectations centered on anticipated benefits associated with certain career paths and anticipated challenges in others, and the comparison of these expectations shaped their career decisions. We found that both extrinsic and intrinsic factors influenced participants’ evaluations of personal priorities and values as they weighed academic versus nonacademic careers. Extrinsic factors: Job stability, salaries, and strategic credentials . Aligning with the trend of increasing employment in industry, many participants were drawn towards non-academic paths due to anticipated positive outcomes. Nonacademic careers were often perceived as offering greater stability compared to academic roles, particularly in the context of the uncertainties surrounding tenure-track positions. For example, Participant 33, who secured a position in the synthetic biology industry, stated that he chose industry over a faculty position due to the perceived “ten years of struggle in the academic world” required to obtain tenure. Additionally, the expectation of financial reward was frequently mentioned. Participants frequently cited higher earning potential in the industry as a key factor. For example, Participant 57, who was originally interested in pursuing an academic/research career at research-intensive universities, described how she reevaluated her career goals after recognizing the economic advantages of nonacademic roles, “When I realized that I could have a fulfilling, much more successful in terms of money, career outside of that track, then it started to become really attractive to me.” Participant 4, a scientist at a biotech company, specifically mentioned the tangible improvements in his financial well-being, “…since grad school, my salary has been tripled since then, which has been really helpful and just everything that comes along with that, increased financial security and just able to support my family….” On the other hand, participant 300, who was in a biotech job searching while completing a postdoc, acknowledged the economic trade-offs, “The postdoc salary is low…I could get a lot more at a biotech company.” These expectations and considerations regarding salaries and compensation highlight how financial incentives intersect with career aspirations. Beyond immediate considerations of salary and stability, some graduates specifically described postdocs in terms of the longer-term, strategic opportunities they were expected to provide. Several participants viewed a postdoc as an expected stage on the academic pathway, essential for being competitive for faculty roles. Participant 295, a postdoc, described it as “the required and expected step before applying to faculty positions,” and Participant 120, a postdoc, similarly noted that “academic science requires that we do this extra training set of postdoc(s) to prove that we're independent researchers.” For those committed to academia, postdocs were framed as critical opportunities to deepen expertise, expand publication records, and demonstrate independence as scientists—what participant 308, a postdoc, called “an important step… to develop both as an independent scientist as well as a member of the team, [and] develop writing and communication skills.” This pattern also extended to several graduates who were open to or would ultimately pursue nonacademic careers. They expected postdoctoral training to enhance their attractiveness for industry roles or to keep multiple options open. Participant 123, a postdoc, explained that going straight into industry “would have been nice,” but felt “very difficult… without doing a postdoc first,” while Participant 157, a postdoc, planned “two, three, four, maybe five years of a postdoc” before deciding on either an academic position or a move to industry. These narratives illustrate graduates’ expectations about the anticipated payoffs of postdoctoral training, including greater competitiveness for faculty roles, stronger research records, and expanded opportunities in industry, which shaped their decisions to pursue postdocs, even when they were uncertain about remaining in academia. Intrinsic factors: Fulfillment and work-life balance . While financial and stability concerns drove many toward industry, others prioritized intrinsic rewards, such as personal fulfillment and work-life balance. Nevertheless, participants’ definitions of fulfillment varied and led them to divergent career paths. For those pursuing careers outside academia, exhaustion from hyper-specialization and the pressure of postdoctoral work were huge considerations. Participant 57, a microscopy specialist, described her shift in interest from a traditional academic research path to industry: I probably would have to do three plus years in a postdoc and get some really high-impact papers, which would have happened eventually if I had stuck it out…I was also just getting a little bit burnt out on the everyday, being just really specialized. I wanted to kind of step back from that…Just reading the same thing over, you just get kind of stuck. I was like, do I want to be doing this forever? Is this gonna be exciting enough for me? Similarly, Participant 88, a preceptor, prioritized work-life balance in her academic-adjacent role, “one of the reasons I took this position is that they do like that balance…I really want something like that. I don't wanna be in the lab for 80 hours pushing out as many publications as I can. I'd like to have that life that I can have.” On the other hand, some participants remained committed to academia despite its challenges, and they valued the alignment of academic career paths with their personal and ethical values. Participant 279, a postdoc, described her sustained passion for academia despite the job market being “rough and daunting”: Doing an academic career is what lines up best with my personality…If I were just working for a pharmaceutical company, they tell you what to work on…they pay you a lot of money but meanwhile, they're making billions, tons and tons of money off of insulin that people require to live. It's just gross to me… I think academia suits my personality the best because I do love research, but overall, I think at least at this point, I find a lot of fulfillment in mentoring students, teaching and stuff. I feel like not only with doing research that hopefully will help someone someday, I like the immediate gratification too, of helping someone, a younger person or whatever learn something new. Here, she acknowledged the reality that the academic job market is rough and daunting, but her career choice was driven by a powerful rejection of the anticipated extrinsic reward found in industry that she deemed ethically compromised. Additionally, the rewards of mentorship and intellectual independence were recurring motivators for those who stayed in academic sectors. As Participant 25, a postdoc, explained, “I always enjoyed having those science career discussions with them, in addition to more of the actual science mentorship discussions. Yeah, I always enjoyed that.” Participant 71 also expressed enjoyment in postdoc work, particularly because of the opportunity to mentor students: Every experience, particularly mentoring [student name], that's such a positive experience for everybody really makes me very open and interested in mentoring students. That's something that I've shared with my boss now that, once we can have undergrads in the lab again, I'm very open to that and would love to do that in the future. Yeah, I would say that just having such a good fulfilling experience makes me want to do more of it…As a postdoc, it really is up to me what we're gonna do next and what we decide to pursue about the data, which has been a nice challenge, I would say. To conclude, these perceived benefits and challenges directly informed participants’ career decisions. Career choices were shaped by a dynamic interplay of self-efficacy, contextual factors, and outcome expectations, with no single factor universally determining their paths. The next section extends this analysis by examining how graduates, looking back from those early career roles, assessed the strengths and gaps of their doctoral preparation in relation to the pathways they pursued. Research Question 2: What Perceived Skill Strengths and Gaps in Doctoral Training do these Graduates Identify in Relation to their Early Career Outcomes? The patterns in Table 1 are reflected in participants’ narrative accounts and help clarify how doctoral preparation was perceived in relation to the career pathways described in RQ1. To provide an overview of patterns across the full sample, we coded the “Perceived Skill Strengths” and “Perceived Skill Gaps” columns in Table 1 into broad categories (e.g., research/technical skills, mentoring, communication, business/management, career development, advanced statistics/programming). Across our sample, the most commonly named strengths were research and technical skills, with about 90% of participants describing scientific techniques, experimental design, data analysis, or similar abilities as areas of confidence, which were areas that aligned well with postdoctoral and research-intensive roles. Some of them also highlighted mentoring, communication, and writing, and soft skills like time- or project-management and independence as strengths. These strengths are often aligned with and contribute to participants’ self-efficacy, as reported in RQ1. A full summary of the strengths is provided in Appendix III. In contrast, the most frequently reported skill gaps were in business and management skills (∼19%) and advanced statistics, coding, or related computational skills (∼21%). Communication, interpersonal skills, and knowledge around career options and job applications are also areas where they wish to receive more training and exposure. Because these perceived gaps more clearly reveal where graduates experienced uneven alignment between doctoral preparation and the early career trajectories, we focused on these gaps in greater detail below. Identified skill gaps and desired further training . Despite reporting strong foundational skills in scientific research and technical skills, participants consistently identified key areas where their doctoral training left them underprepared. Many participants felt their training did not adequately address skills needed outside academia and research, especially those who pursued non-academic careers. These perceived skill gaps were concentrated around business, advanced quantitative, and computational training—areas increasingly important for various postdoctoral career paths. Business and organizational skills . A clear lack of preparation in core business skills was particularly salient for participants transitioning into non-academic roles. For example, Participant 187, a consultant, listed interview skills and management as areas where he felt underprepared, stating, “They're not training people to go into business. They're training people to go into science.” Similarly, Participant 11, also a consultant, listed business as a skill where he felt a gap. On the other hand, Participant 310, who started a Master of Commerce after getting her PhD, shared her motivation: The reason I decided to not get a job is because I felt—ultimately what I think now that I want to do is get into business development, and I think going to this program would allow me to start closer to that type of job responsibility rather than trying to find a job that I would most likely start off as being a scientist and then have the hopes of getting into more of business operations without any business background. Nevertheless, this gap was also reported by a few graduates pursuing an academic path. Participant 258, a clinical microbiology fellow, shared, “I wish that I had taken some business classes… in the role that I would eventually be in as a director, I really need that business background… I'm teaching myself that now… but it's something that I wish I had started earlier.” Similarly, Participant 302, who was unemployed at the time, emphasized the need for more targeted preparation, stating, “I wish I had training in business environments. Those are the two things I—big ones I wish I had.” Advanced statistical and computational methods . Regardless of career path, many participants found basic statistical training inadequate for handling the growing complexity and scale of data in modern biology. Coding, statistical modeling, and machine learning were often cited by participants as areas where more structured training would have been beneficial. Participant 12, a consultant, reflected, “I just didn't get a lotta teaching into it, but I think coding—that's been something I wish I had more experience on and was better at during—from grad school into now.” Participant 300, a postdoc, echoed this concern, “I wish I had taken a little more time to formally learn more statistics in machine learning…There're still now a few things that I am missing.” Similarly, Participant 206, who worked at product development at a biotech company, and Participant 71 both cited a desire for deeper statistical and programming instruction: I wish I knew more statistics. I skated through with the very basics classwork, and my PIs didn't really use statistics, so no one was going, “Hey, you need to learn this.” Now, I use them a lot, and I don't really know what I'm doing. I'm tryin’ to see if there's another nontechnical skill that I wish I had learned in grad school. —Participant 206 Oh, this is one I've thought about a lot. There are two big ones: statistics and programming. Those are two things that I think, in every opportunity a graduate program has ever asked about what they could do better, those are the two because, in biology, we do not learn enough statistics at all, and we're really, really bad at statistics. —Participant 71 Taken together, these findings suggest that participants generally felt well prepared in core research skills, while more often identifying gaps in broader career development support and discrete skill domains (e.g., business, statistics). DISCUSSION This study investigates biology PhD graduates’ career outcomes and the factors shaping those outcomes by analyzing interview data from biology PhD graduates. Guided by Social Cognitive Career Theory (SCCT), we examined graduates’ self-efficacy, the contextual supports and barriers they encountered, and their outcome expectations regarding academic and nonacademic paths. We also foregrounded graduates’ assessments of the strengths and gaps in their doctoral training and how they connected these perceived strengths and gaps to their early decisions. Our findings contribute to the growing discourse on the shift of STEM PhD graduates toward nonacademic careers by centering students’ retrospective views of how they understood their skills, environments, and options as they left their programs—an area that remains understudied in the existing literature. Differentiated Self-Efficacy Findings from this study extend existing scholarship in several ways. Consistent with previous work showing that biology and other STEM PhD programs are highly effective at building research and technical expertise but less systematic in developing broader professional skills ( Fuhrmann, 2016 ; Ferrie and Scott, 2021 ), graduates in our study emphasized research/scientific matters as domains in which they felt particularly strong, and explicitly connected these proficiencies to research-intensive roles they saw as feasible, such as postdocs and some research-oriented positions in industry. In SCCT terms, these narratives reflect differentiated beliefs about capabilities ( Lent et al. , 2022 ), as participants felt highly efficacious in doing and communicating science within familiar research settings, but were less confident in domains where business skills, project management, and communication with non-specialist audiences are needed. These differentiated beliefs help explain why research-focused roles, and particularly postdocs, often appeared as the natural next step, even for graduates who were open to nonacademic careers, since the roles they saw as most aligned with what they believed they could already do well. Additionally, these interpretations align with scholarship on STEM workplace skills and on the scientific rules, roles, and values that shape how life sciences trainees understand what it means to be a scientist ( Jang, 2016 ; Busch et al ., 2024 ). Mentoring also emerged as a domain in which some postdocs felt especially capable and energized. Previous work in biology education has shown that graduate students and postdocs often play important mentoring roles for undergraduate researchers ( Dolan and Johnson, 2010 ; Aikens et al ., 2016 ). Our findings add nuance by showing that, in graduates’ retrospective accounts, mentoring was not only a routine responsibility but also a source of enjoyment and perceived capability, which may have reinforced the attractiveness of postdoctoral roles for those who valued engagement in training and research environments ( Lent et al ., 1994 ; Wang et al ., 2022 ). In SCCT terms, these narratives suggest that mentoring-related self-efficacy, coupled with positive expected outcomes from such work, helped shape how some graduates understood postdoctoral pathways as personally fitting options. Academic-Centric Advising as a Contextual Constraint We found that although most advisors were supportive, their academic-centric perspectives and limited familiarity with nontraditional paths often left students without effective mentorship or guidance. This pattern is consistent with previous work showing that faculty advisors in the life sciences frequently lack information about, or hold biases against, nonacademic careers, which can constrain the range of options students feel able to pursue ( Sauermann and Roach, 2012 ; St. Clair et al ., 2017 ). The lack of exposure to industry role models and networks further reinforced academic norms—such as the assumption that postdoctoral training is essential—even in contexts where such beliefs may not apply. This echoes with research on how local cultures of training shape what graduate students see as “legitimate” or prestigious paths ( Austin, 2002 ; Gardner, 2008 ). From an SCCT perspective, these advising relationships and lab norms constitute contextual supports and barriers by shaping which options feel supported and realistic, even when graduates’ skills might be transferable across a wider range of careers. Trading Off Outcome Expectations Participants in our sample reported trading off outcome expectations for different careers. For example, postdocs were often characterized as allowing graduates to mentor students and remain closely connected to research they found intellectually engaging, but at the cost of short-term contracts, intensive lab time, and delayed financial stability. Additionally, for some participants, postdoctoral training is no longer imagined solely as preparation for faculty careers ( Sauermann and Roach, 2016 ) but also as credentials that would make them more competitive for industry roles or allow them to “keep options open” between academia and industry ( Gibbs et al ., 2015 ; Mathur et al ., 2015 ). These findings suggest that career decisions were often not driven by a single consideration but by how graduates balanced multiple anticipated outcomes, including financial security, stability, alignment with personal values, and their sense of scientific identity. Some graduates moved away from academic pathways when they anticipated burnout, prolonged training, or excessive specialization, whereas others remained committed to academia because they perceived it with ethical alignment, mentoring, and personal fulfillment. In SCCT terms, this interplay reflects the role of outcome expectations in shaping choices among paths that graduates perceived as feasible based on their skills and previous training. Understanding biology PhD graduates’ career choices requires attention not only to their skills and context, but also to how they actively trade off competing outcome expectations across different roles ( Roach and Sauermann, 2017 ). Retrospective Accounts of Skill Preparation in Relation to Early Career Trajectories Finally, our findings reported in RQ2 extend prior research on skill preparation in doctoral education (e.g., Fuhrmann, 2016 ; De Grande et al. , 2011 ) by centering the retrospective reflections of biology PhD graduates in early career roles. Looking back from concrete post-PhD positions, graduates identified particular gaps (e.g., business skills, advanced statistics and programming, project management) that became salient when they encountered the everyday demands of their current roles. Many participants framed these gaps not only as missing content, but as missed opportunities to build confidence and to see a wider range of careers as genuinely attainable. These retrospective accounts extend the findings from RQ1 by showing how graduates later interpreted the relationship between their training and their early career trajectories. Specifically, these reflections illustrated how their doctoral programs did or did not cultivate beliefs about their capabilities in key domains, provide contextual support for exploring diverse paths, and shape their expectations about the outcomes associated with academic and nonacademic careers. Some strengths reinforced confidence in roles closely tied to research training, whereas some gaps became visible when graduates encountered work environments that demanded different forms of expertise or career knowledge. Thus, the contribution of RQ2 is not only to identify areas of preparation that graduates perceived as strong or weak, but also to show how those perceptions reveal uneven alignment between doctoral training and the range of early career pathways biology PhDs pursued. Data-wise speaking, our findings both align with and extend previous work examining PhD students’ career outcomes using the ECR dataset. Similar to Blaney et al .’s ( 2022 ) analysis of biology PhD students’ trajectories, graduates in our sample emphasized the importance of advisor relationships in shaping how they navigate their options during training. Participants frequently reported periods when advisor messages were narrow or when they felt they had little information about alternative paths. This pattern is consistent with previous studies’ findings of students describing feeling constrained by limited information and the importance of PI's openness in shaping future career aspirations and opportunities ( Mathur et al ., 2015 ; Blaney et al ., 2022 ). At the same time, our study extends this work by moving from students’ anticipation of possible futures during the PhD to graduates’ retrospective accounts from the job market. This approach allows us to examine how the patterns identified in previous works are taken up in graduates’ later interpretations of whether their programs prepared them for the work they now do. In doing so, we respond to calls for research that centers recent PhD graduates’ perspectives ( Fuhrmann et al ., 2016 ) to inform revisions to graduate curricula and mentoring that better support diverse career pathways and more intentional career development across academic and nonacademic roles. Limitations While this study offers valuable insights into biology PhD graduates’ career outcomes and decision-making processes, we acknowledge several limitations of using secondary data. First, as a secondary data source, the interview questions and structure were not designed specifically to answer the research questions guiding this study. As such, certain dimensions of participants’ experiences may not have been explored in sufficient depth or with the theoretical lens of Social Cognitive Career Theory (SCCT) in mind. For example, our use of SCCT in this study focuses primarily on the contextual factors at the level most visible in the interviews: advisor relationships, lab and departmental cultures, funding structures, and access to professional-development opportunities. We do not model contextual factors at the level of institutional category or specific program design, in part because the qualitative dataset does not include systematic indicators of those features for all participants. It is possible that some of the gaps graduates describe are concentrated in particular institutional contexts, while others are more widespread. Our contribution is to show how graduates make sense of the environments they personally experienced, not to adjudicate which types of programs or universities are most or least effective. Moreover, the sample includes students from 53 research-intensive universities, approximately 80% of which are classified as R1 institutions. This emphasis may limit the transferability of the findings to PhD students in other institutional contexts (e.g., R2 institutions, teaching-focused universities). The overrepresentation of R1 environments may reflect a training experience more heavily oriented toward academic research careers ( Bennion et al. , 2022 ). Additionally, we do not have systematic program-level information for each department. It is possible that some departments or institutions offer more extensive career preparation and broader skill training than others, and that some of the gaps graduates describe are concentrated in particular institutional contexts ( Tomlinson-Clarke et al ., 2019 ; Soler, 2025 ). Nevertheless, our goal is to identify patterns in how biology PhD graduates from research-intensive settings describe their training and early career decisions and make sense of the environments they personally experienced, using their interpretations of supports, barriers, and preparedness as theoretically meaningful outcomes. Therefore, our findings should be read as indicating recurring concerns and strengths across this sample, not as evidence that every biology PhD program exhibits the same limitations. Addressing this will require future studies that link graduate narratives to systematic institutional and program-level indicators to test whether specific gaps cluster by program type. Furthermore, while we included all participants who completed their degrees within the time frame of the study, the qualitative analysis is based only on the subset of participants for whom interview data were available. We have limited ability to systematically characterize “other training” that participants may have received in addition to their doctoral programs. In the interviews, some graduates described experiences such as internships, but these opportunities were not documented in a consistent way across all participants. As a result, we treat such experiences as part of individuals’ narratives rather than as variables that can be compared across cases or connected to outcomes. Previous work suggests that structured professional-development programs and internships can partially compensate for gaps in traditional research-centric training (e.g., Fuhrmann et al ., 2016 ; St. Clair et al. , 2017 ; Mathur et al., 2023 ), which means that some graduates in our sample may have developed relevant skills outside their home departments. Our analysis, therefore, focuses on how graduates interpret the preparation they associate with their doctoral and immediate post-PhD environments, and we recognize that their broader training histories might be more complex than what's captured in the data. A further limitation concerns the timing of the data. The participants in this study entered doctoral programs in Fall 2014 and navigated early career transitions in the late 2010s and early 2020s. Given ongoing changes in doctoral education, labor market conditions, and institutional career-development efforts, the findings should not be generalized as a direct representation of all biology PhD programs today. Instead, they point to questions that merit continued examination in more recent cohorts and across different program contexts. Future research could build on these findings by conducting interviews specifically designed around SCCT to capture more targeted insights into doctoral career decision-making. Additionally, expanding the sample to include graduates from a wider range of institutional types could improve the transferability of future studies. Implications Rather than relying solely on employment statistics to infer programmatic needs, this study identifies biology PhD graduates’ perceived strengths and areas of under-preparation using students’ self-assessments. Our results do not suggest that biology PhD programs should abandon their core focus on research, but indicate that research-intensive training alone may not, by itself, help all graduates recognize how their skills translate across different professional contexts, or provide equally robust support for navigating a wider range of career options. Therefore, the findings suggest a gap in how programs could support them in developing broader skills and career knowledge that make a wider set of roles feel attainable. For readers involved in designing or leading biology graduate programs, our findings point to several concrete areas where curricula and mentoring structures could be strengthened. Graduates in our sample did not describe their programs as uniformly deficient; rather, they emphasized strong preparation in core research domains alongside recurring gaps in skills and supports that became salient once they entered the job market. In this section, we highlight implications for modernizing PhD training and for supporting faculty in providing more holistic career mentorship. Modernize PhD training with the necessary skills . Doctoral programs may benefit from complementing their traditional focus on academic research preparation with curricular and career-development supports that more explicitly address diverse career pathways. As reported by many participants, especially those entering nonacademic sectors, a critical area for reform is the training of business skills. Business skills—such as organizational leadership, entrepreneurship, and industry-specific practices—are valued by employers across fields ( Jackson and Chapman, 2009 ; Tokarčíková et al ., 2020 ). Graduate programs are encouraged to integrate these topics into the formal curriculum, for example, through recommended elective courses and modules, rather than leaving them to optional workshops that tend to draw only a self-selected subset of students ( Schweickart et al ., 2023 ). Such training would better prepare students for a range of roles, including consulting, startups, and corporate research and development (R&D), where business acumen is increasingly valued. Likewise, the findings of this study highlight that biology PhD graduates report the importance of advanced statistical skills in their training and careers. Previous studies have also argued that workers with statistical skills are needed across a wide range of fields and industries in this rapidly changing labor market ( Carlson, 2002 ; Garvin and Watzlaf, 2004 ; Gibbs, 2010 ; Astuti et al ., 2025 ). Statistical literacy is important for both research and nonresearch careers across sectors ( Aerts et al ., 2021 ; Pudjiastuti, 2022 ). The findings also suggest value in providing more structured opportunities for hands-on training in tools such as Python, R, and other statistical or data analysis software as part of their core or methods curriculum, either through departmental curriculum or partnerships with data science programs. Strengthening statistics skill sets will enhance biology PhDs’ competitiveness across diverse career pathways and help meet the digital demands of modern workplaces. Recent national efforts have recognized the need to modernize biology PhD training, as exemplified by the National Institute of General Medical Sciences’ (NIGMS) Predoctoral Training Grant ( NIGMS, 2016 ) and the Opportunities for Professional Training in Occupations for Scientists (OPTIONS) program at Johns Hopkins University ( JHU, n.d ). Both initiatives expand career exploration and skill-building opportunities for PhD students, aiming to better prepare them for a broad range of scientific careers beyond academia. Addressing faculty limitations in career mentorship . Another significant gap identified in this study is the limited capacity of faculty advisors to guide students toward nonacademic careers, which may come from the lack of pedagogical training and limited time, as suggested by prior relevant literature ( Dolan and Johnson, 2010 ; Brownell and Tanner, 2012 ). Additionally, faculty members’ strong academic-centric mentorship and personal biases toward academia partly contribute to the persistence of postdocs as the default next step. Advisors often rely on their own field expertise and personal academic experiences when giving career advice ( Twiton, 2020 ). Institutions should invest in professional development programs to help faculty understand the diversity of career options available to PhDs and provide informed guidance. Furthermore, it is critical for faculty to adopt a more open mindset and recognize that academic positions are not the only prestigious or valuable career paths available to their trainees. Sauermann and Roach (2012) found that faculty in life sciences often strongly encourage academic careers while discouraging industry careers, further entrenching this academic bias. Encouraging faculty to broaden their perspectives can help foster a more supportive environment for students considering diverse career trajectories and promote greater alignment between training and labor market realities ( Mangematin, 2000 ; Stephan, 2012 ). Additionally, providing incentives for PIs to engage with and support students’ exploration of nonacademic career options could be an effective strategy. A recent study by Mathur et al . (2023) on Wayne State University's NIH-funded Broadening Experiences in Scientific Training (BEST) program provided a compelling example. The BEST is a career development training program that aims to prepare biomedical PhD students for diverse careers through structured career development training. Faculty who participated in BEST reported increased support for students pursuing nonacademic research careers, greater respect for these pathways, and improved ability to guide career exploration—outcomes that positively correlated with their level of engagement. Although this study did not systematically assess whether participants had access to similar training opportunities, the BEST model demonstrates one way programs might strengthen career guidance while maintaining a strong research focus. When faculty are equipped with knowledge and resources, they become powerful advocates for diversifying career preparation. However, systemic commitment is required to sustain such progress. At the same time, our findings suggest that relying solely on individual advisors to provide comprehensive career mentorship is unlikely to be sufficient. This finding aligns with previous work on US PhD programs, which shows that students with nonacademic career interests often perceive lower support from their advisors and programs, and are less likely to seek career guidance from faculty, in part because advisors know little about nonacademic paths and hold traditional academic expectations ( Sherman et al ., 2021 ). On the other hand, awareness and access to program- or institution-level resources, such as Individual Development Plans ( Hobin et al ., 2014 ) and Career and Professional Development ( Subramanian et al ., 2022 ), remain insufficient and inequitable. For biology graduate programs, this points to the value of building complementary structures, such as secondary mentors outside the primary lab, a formal connection to alumni working in diverse sectors, and normalizing participation in institutional-level resources across labs, which could share responsibility for exposing students to multiple career paths. Such structures can reduce the consequences of advisor-specific biases while still recognizing the central role that faculty mentors play in students’ development. CONCLUSION Overall, this study uses Social Cognitive Career Theory to understand how biology PhD graduates, looking back from their first post-PhD positions, connect their self-efficacy, contextual supports and barriers, and outcome expectations to the early career roles they pursue. We show that graduates generally feel strongly prepared to “do science,” while also identifying gaps in business and management skills, advanced statistics and programming, and structured career development support. These perceived strengths and gaps, together with the contextual support and norms encountered from advisors and departments, shape which roles feel feasible and desirable, helping explain why many graduates pursue postdocs even as they consider moving into nonacademic careers later. Our findings suggest that aligning doctoral training with the careers biology PhDs pursued will require not only maintaining high-quality research training but also intentionally embedding broader skill-building and career-development opportunities into curricula and advising structures, so that students can see a wider range of paths as genuinely attainable and well matched to their evolving capabilities and goals. Supporting information cbe-25-ar25-s001.pdf (233.8KB, pdf) Footnotes 1 For NCSES reporting, “biological and biomedical sciences” refers to a broad field-of-doctorate category that includes multiple biology-related subfields. 2 In this paper, we use “postdoctoral training/postdoc” to refer to a time-limited, mentored research training/appointment undertaken after completion of the PhD, typically intended to deepen research expertise, expand publications, and build independence as a scientist. 3 The ECR data currently describe the project in two slightly different ways: the Open Science Framework repository ( https://osf.io/hymus/overview ) description states that the data include 336 biology Ph.D. students from 53 U.S. research universities, whereas the current project description ( https://cehs.usu.edu/itls/projects/early-career-research/ ) refers to “a longitudinal sample of 286 biology Ph.D. students followed through the later stages of graduate school and into the early scientific workforce”. This appears to reflect different project scopes or phases. In this paper, we use 336 students to describe the broader study context and original project sample. REFERENCES Aerts, M., Molenberghs, G., & Thas, O. (2021). Graduate education in statistics and data science: The why, when, where, who, and what. Annual Review of Statistics and Its Application, 8(1), 25–39. [ Google Scholar ] Aikens, M. L, Sadselia, S., Watkins, K., Evans, M., Eby, L. T, & Dolan, E. L (2016). A social capital perspective on the mentoring of undergraduate life science researchers: An empirical study of undergraduate–postgraduate–faculty triads. CBE—Life Sciences Education, 15(2), ar16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Arbeit, C., Yamaner, M. I, & National Center for Science and Engineering Statistics (NCSES).(2021) Trends for graduate student enrollment and postdoctoral appointments in science, engineering, and health fields at U.S. academic institutions between 2017 and 2019. NSF 21-317. Alexandria, VA: National Science Foundation. Available at Retrieved from https://ncses.nsf.gov/pubs/nsf21317/ Association of American Universities, Committee on Postdoctoral Education. (1998). Report and recommendations. Astuti, A., Suryawati, E., Suanto, E., Yuanita, P., & Noviana, E. (2025). Charting a course: Exploring computational thinking skills in statistics content among junior high school students. Journal of Pedagogical Research, 9(1), 182–202. [ Google Scholar ] Athanasiadou, R., Bankston, A., Carlisle, M., Niziolek, C. A., & McDowell, G. S. (2018). Assessing the landscape of US postdoctoral salaries. Studies in Graduate and Postdoctoral Education, 9(2), 213–242. [ Google Scholar ] Auriol, L., Misu, M., & Freeman, R. A (2013). Careers of doctorate holders: Analysis of labour market and mobility indicators. OECD science, technology and industry working papers, No. 2013/4, Paris: OECD Publishing. [ Google Scholar ] Austin, A. E (2002). Preparing the next generation of faculty: Graduate school as socialization to the academic career. The Journal of Higher Education, 73(1), 94–122. [ Google Scholar ] Bakken, L. L., Byars-Winston, A., & Wang, M. F. (2006). Viewing clinical research career development through the lens of social cognitive career theory. Advances in Health Sciences Education, 11, 91–110. [ DOI ] [ PubMed ] [ Google Scholar ] Bandura, A., & Wessels, S. (1997). Self-efficacy (pp. 4–6). Cambridge: Cambridge University Press. [ Google Scholar ] Beltramo, J. P., Paul, J. J., & Perret, C. (2001). The recruitment of researchers and the organisation of scientific activity in industry. International Journal of Technology Management, 22(7-8), 811–834. [ Google Scholar ] Bennion, E. A., Lineberger, M. E., & Loepp, E. D. (2022), Preparing for a career at a regional comprehensive university, in Lorentz K. G., Mallinson D. J., Hellwege J. M., Phoenix D. L., & Strachan J. C. (Eds.), Strategies for navigating graduate school and beyond, (pp. 249–254). American Political Science Association. [ Google Scholar ] Bishop, R. M., & Bieschke, K. J. (1998). Applying social cognitive theory to interest in research among counseling psychology doctoral students: A path analysis. Journal of Counseling Psychology, 45(2), 182. [ Google Scholar ] Blaney, J. M., Wofford, A. M., Jeong, S., Kang, J., & Feldon, D. F. (2022). Autonomy and privilege in doctoral education: An analysis of STEM students’ academic and professional trajectories. The Journal of Higher Education, 93(7), 1037–1063. [ Google Scholar ] Blaney, J., Wofford, A., Kang, J., & Feldon, D. F. (2020). Mentoring relationships between doctoral students and postdocs in the lab sciences. Studies in Graduate and Postdoctoral Education, 11(3), 263–279. [ Google Scholar ] Brownell, S. E., & Tanner, K. D. (2012). Barriers to faculty pedagogical change: Lack of training, time, incentives, and… tensions with professional identity? CBE—Life Sciences Education, 11(4), 339–346. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Busch, C. A., Nadile, E. M., Mohammed, T. F., Gin, L. E., Brownell, S. E., & Cooper, K. M. (2024). The scientific rules, roles, and values that life sciences doctoral students want to see upheld by undergraduate researchers. Journal of Research in Science Teaching, 61(10), 2405–2443. [ Google Scholar ] Butz, A. R., Byars-Winston, A., Leverett, P., Branchaw, J., & Pfund, C. (2018). Promoting STEM trainee research self-efficacy: A mentor training intervention. Understanding Interventions, 9(1), 3730. [ PMC free article ] [ PubMed ] [ Google Scholar ] Carlson, B. (2002). Preparing Workers for the 21st Century: The Importance of Statistical Competencies. In proceedings of the International Conference on Teaching Statistics (ICOTS-6), Cape Town, South Africa: (1–6). [ Google Scholar ] Clement, L., Leung, K. N., Lewis, J. B., & Saul, N. M. (2020). The supervisory role of life science research faculty: The missing link to diversifying the academic workforce? Journal of Microbiology & Biology Education, 21(1),  10.1128/jmbe.v21i1.1911 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Connolly, M. R., Lee, Y. G., & Savoy, J. N. (2018). The effects of doctoral teaching development on early-career STEM scholars’ college teaching self-efficacy. CBE—Life Sciences Education, 17(1), ar14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cordova, C. G. (2022). The Career Decision-Making Self-Efficacy and Career Action Steps of Humanities Students: A Quantitative Survey Analysis (Doctoral dissertation). Tampa, FL: University of South Florida. [ Google Scholar ] Cyranoski, D., Gilbert, N., Ledford, H., Nayar, A., & Yahia, M. (2011). Education: The PhD factory. [ DOI ] [ PubMed ] [ Google Scholar ] De Grande, H., De Boyser, K., Vandevelde, K., & Van Rossem, R. (2011). The skills mismatch: What doctoral candidates and employers consider important? ECOOM, 4, 1–4. [ Google Scholar ] Denton, M., Borrego, M., & Knight, D. B. (2022). US postdoctoral careers in life sciences, physical sciences and engineering: Government, industry, and academia. PLoS One, 17(2), e0263185. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Dewett, T., Shin, S. J., Toh, S. M., & Semadeni, M. (2005). Doctoral student research as a creative endeavour. College Quarterly, 8(1), n1. [ Google Scholar ] Didiano, T. J., Wilkinson, L., Turner, J., Franklin, M., Anderson, J. H., Bussmann, M., … Audet, J. (2019). I have a Ph. D.! now what? A program to prepare engineering Ph. D.’s and postdoctoral fellows for diverse career options. Paper presented at the American Society for Engineering Education Annual Conference & Exposition, Tampa, FL. Retrieved from: https://www.asee.org/public/conferences/140/papers/26276/ [ Google Scholar ] Diehl, P. F. (2021). Adapting to the changing academic job market. Political Science Today, 1(4), 13–15. [ Google Scholar ] Dolan, E. L., & Johnson, D. (2010). The undergraduate–postgraduate–faculty triad: Unique functions and tensions associated with undergraduate research experiences at research universities. CBE—Life Sciences Education, 9(4), 543–553. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Enders, J. (2005). Border crossings: Research training, knowledge dissemination and the transformation of academic work. Higher Education, 49, 119–133. [ Google Scholar ] Feldon, D. F., Litson, K., Roksa, J., & Griffin, K. (2023). Trajectories in early career research: Data repository.  10.17605/OSF.IO/HYMUS [ DOI ] Ferrie, J. M., & Scott, A. (2021). The PhD and missing skills: Capacity and capability building for social science engagement with industry. Journal of Perspectives in Applied Academic Practice, 9(3), 22–31. [ Google Scholar ] Fuhrmann, C. N. (2016). Enhancing graduate and postdoctoral education to create a sustainable biomedical workforce. Human Gene Therapy, 27(11), 871–879. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gardner, S. K. (2008). Fitting the mold of graduate school: A qualitative study of socialization in doctoral education. Innovative Higher Educationc, 33(2), 125–138. [ Google Scholar ] Garrison, H. H., Justement, L. B., & Gerbi, S. A. (2016). Biomedical science postdocs: An end to the era of expansion. The FASEB Journal, 30(1), 41–44. [ DOI ] [ PubMed ] [ Google Scholar ] Garvin, J. H., & Watzlaf, V. J. (2004). Current coding competency compared to projected competency. Perspectives in Health Information Management/AHIMA, American Health Information Management Association, 1, 2. [ PMC free article ] [ PubMed ] [ Google Scholar ] Gibbs Jr, K. D., McGready, J., & Griffin, K. (2015). Career development among American biomedical postdocs. CBE—Life Sciences Education, 14(4), ar44. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gibbs, G. R. (2010). Mathemetics and Statistics Skills in the Social Sciences. The Maths, Stats and OR Network. Griffin, K. A., Miller, C., & Roksa, J. (2023). Agency and career indecision among biological science graduate students. Studies in Graduate and Postdoctoral Education, 14(1), 99–113. [ Google Scholar ] Grinstein, A., & Treister, R. (2018). The unhappy postdoc: A survey based study. F1000Research, 6, 1642. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hernandez, I. A., Segura, O. M., Gonzalez, R. R., Flores, L., Villodas, M. T., Sohl, C. D., … & Thoman, D. B. (2024). The making of future scientists: Faculty mentor cultural awareness and inclusive science labs. CBELife Sciences Education, 23(3), ar36. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hobin, J. A., Clifford, P. S., Dunn, B. M., Rich, S., & Justement, L. B. (2014). Putting PhDs to work: Career planning for today's scientist. CBE—Life Sciences Education, 13(1), 49–53. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jackson, D., & Chapman, E. (2009). Business graduate skills sets – summary report. Crawley: Graduate School of Education. The University of Western Australia. [ Google Scholar ] Jang, H. (2016). Identifying 21st century STEM competencies using workplace data. Journal of science education and technology, 25(2), 284–301. [ Google Scholar ] Jeong, S., Litson, K., Blaney, J., & Feldon, D. F. (2020). Shifting gears: Characteristics and consequences of latent class transitions in doctoral socialization. Research in Higher Education, 61, 1027–1053. [ Google Scholar ] Johns Hopkins University . (n.d.). OPTIONS curriculum: Guiding PhDs toward diverse career success. Imagine Center. https://imagine.jhu.edu/resources/options-curriculum/ Johnson, D. R. (2018). The boundary work of commercialists in academe: Implications for postdoctoral training. The Journal of Higher Education, 89(4), 503–526. [ Google Scholar ] Kemp, M. W., Newnham, J. P., & Chapman, E. (2012). The biomedical doctorate in the contemporary university: Education or training and why it matters. Higher Education, 63, 631–644. [ Google Scholar ] Kim, K., & Karau, S. J. (2009). Working environment and the research productivity of doctoral students in management. Journal of Education for Business, 85(2), 101–106. [ Google Scholar ] Larson, R. C., Ghaffarzadegan, N., & Xue, Y. (2014). Too many PhD graduates or too few academic job openings: The basic reproductive number R0 in academia. Systems Research and Behavioral Science, 31(6), 745–750. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. [ Google Scholar ] Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. Career choice and development, 4(1), 255–311. [ Google Scholar ] Lent, R. W., Morris, T. R., Wang, R. J., Moturu, B. P., Cygrymus, E. R., & Yeung, J. G. (2022). Test of a social cognitive model of proactive career behavior. Journal of Career Assessment, 30(4), 756–775. [ Google Scholar ] Li, L., & Wu, J. (2025). Beyond the barriers: Examining the socialization experiences of STEM international doctoral students in research‐intensive universities. Studies in Graduate and Postdoctoral Education, 1–22. [ Google Scholar ] Lin, E. S., & Chiu, S. Y. (2016). Does holding a postdoctoral position bring benefits for advancing to academia? Research in Higher Education, 57, 335–362. [ Google Scholar ] Lu, J., Velten, B., Klaus, B., Ramm, M., Huber, W., & Coulthard-Graf, R. (2023). Meta-research: The changing career paths of PhDs and postdocs trained at EMBL. Elife, 12, e78706. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] MacMillan, T., Wang, D., Otuyelu, F., & Hill, A. (2023). Perceptions of doctoral programs’ training and preparedness for academic careers. Journal of Social Work Education and Practice, 8(1), 19–29. [ Google Scholar ] Mangematin, V. (2000). PhD job market: Professional trajectories and incentives during the PhD. Research Policy, 29(6), 741–756. [ Google Scholar ] Mathur, A., Meyers, F. J., Chalkley, R., O'Brien, T. C., & Fuhrmann, C. N. (2015). Transforming training to reflect the workforce. Science Translational Medicine, 7(285), 285ed4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mathur, A., Hwalek, M., Straub, V., & Chow, C. S. (2023). Increasing faculty support, respect, and ability to help doctoral students explore non-academic research career opportunities. Heliyon, 9(1), e13052. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] McAlpine, L., & Emmioglu, E. (2015). Navigating careers: Perceptions of sciences doctoral students, post-PhD researchers and pre-tenure academics. Studies in Higher Education, 40(10), 1770–1785. [ Google Scholar ] McCain, J., & Roksa, J. (2023). Beyond technical mastery: Inequality in doctoral research skill development in the biological sciences. Studies in Graduate and Postdoctoral Education, 14(3), 332–346. [ Google Scholar ] McGaskey, F. G. (2015). Facilitating the creation of knowledge: An investigation of the factors that influence the research productivity of Black doctoral students at predominantly White institutions. Journal of Negro Education, 84(2), 187–201. [ Google Scholar ] Melin, G., & Janson, K. (2006). What skills and knowledge should a PhD have? Changing preconditions for PhD education and post doc work. Wenner Gren International Series, 83, 105. [ Google Scholar ] Mowbray, S., & Halse, C. (2010). The purpose of the PhD: Theorising the skills acquired by students Higher Education Research & Development, 29(6), 653–664. [ Google Scholar ] National Academy of Sciences, Policy, Global Affairs, Committee on Science, Public Policy, & Committee to Review the State of Postdoctoral Experience in Scientists. (2014). The postdoctoral experience revisited. Washington, D.C.: The National Academies Press. [ Google Scholar ] National Center for Science and Engineering Statistics (NCSES). (2024). Doctorate Recipients from U.S. Universities: 2023. NSF 25-300. Alexandria, VA: U.S: National Science Foundation. Available at Retrieved from https://ncses.nsf.gov/pubs/nsf25300 [ Google Scholar ] National Institute of General Medical Sciences . (2016). Request for Information (RFI): Strategies for modernizing biomedical graduate education (Notice No. NOT-GM-16-109). National Institutes of Health. href="https://grants.nih.gov/grants/guide/notice-files/not-gm-16-109.html [ Google Scholar ] Nersesian, P. V., Starbird, L. E., Wilson, D. M., Marea, C. X., Uveges, M. K., Choi, S. S. W., … Cajita, M. I. (2019). Mentoring in research-focused doctoral nursing programs and student perceptions of career readiness in the United States. Journal of Professional Nursing, 35(5), 358–364. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Powell, K. (2015). The future of the postdoc. Nature, 520(7546), 144–148. [ DOI ] [ PubMed ] [ Google Scholar ] Pudjiastuti, S. R. (2022). The role of statistics in research to improve critical thinking skills. Journal of Humanities and Social Studies, 6(3), 417–422. [ Google Scholar ] Purcell, K., Elias, P., Durbin, S., Davies, R., & Warren, S. (2006). The employment of social science PhDs in academic and non-academic jobs: Research skills and postgraduate training. Swindon: Economic and Social Research Council. [ Google Scholar ] Roach, M., & Sauermann, H. (2017). The declining interest in an academic career. PloS One, 12(9), e0184130. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Roksa, J., Feldon, D. F., & Maher, M. (2018). First-generation students in pursuit of the PhD: Comparing socialization experiences and outcomes to continuing-generation peers. The Journal of Higher Education, 89(5), 728–752. [ Google Scholar ] Roksa, J., Wang, Y., Feldon, D. F., & Ericson, M. (2022). Who is publishing articles during graduate school? Race and gender inequalities in biological sciences over time. Journal of Diversity in Higher Education, 15, 47–57. [ Google Scholar ] Roy, S., Velasco, B., & Edwards, M. A. (2024). Competition for engineering tenure-track faculty positions in the United States. PNAS nexus, 3(5), pgae169. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. [ DOI ] [ PubMed ] [ Google Scholar ] Saldaña, J. (2021). Coding techniques for quantitative and mixed data. The Routledge Reviewer's Guide to Mixed Methods Analysis (2021), 151–160. [ Google Scholar ] Sarrico, C. S. (2022). The expansion of doctoral education and the changing nature and purpose of the doctorate. Higher Education, 84(6), 1299–1315. [ Google Scholar ] Sauermann, H., & Roach, M. (2012). Science PhD career preferences: Levels, changes, and advisor encouragement. PloS One, 7(5), e36307. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sauermann, H., & Roach, M. (2016). Why pursue the postdoc path? Science, 352(6286), 663–664. [ DOI ] [ PubMed ] [ Google Scholar ] Schunk, D. H. (1995). Self-efficacy, motivation, and performance. Journal of Applied Sport Psychology, 7(2), 112–137. [ Google Scholar ] Schweickart, T., Hill-Whilton, Z., Chitale, S., Cobos, D., Gilon-Yanai, M., Achuonjei, J., … Gold-von Simson, G. (2023). The biomedical entrepreneurship skills development program for the advancement of research translation: Foundations of biomedical startups course, metrics, and impact. Journal of Clinical and Translational Science, 7(1), e77. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Seo, G., & Yeo, H. T. (2020). In pursuit of careers in the professoriate or beyond the professoriate: What matters to doctoral students when making a career choice? International Journal of Doctoral Studies, 15, 615. [ Google Scholar ] Seo, G., Ahn, J., Huang, W. H., Makela, J. P., & Yeo, H. T. (2021). Pursuing careers inside or outside academia? Factors associated with doctoral students’ career decision making. Journal of Career Development, 48(6), 957–972. [ Google Scholar ] Sherman, D. K., Ortosky, L., Leong, S., Kello, C., & Hegarty, M. (2021). The changing landscape of doctoral education in science, technology, engineering, and mathematics: PhD students, faculty advisors, and preferences for varied career options. Frontiers in Psychology, 12, 711615. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sinche, M., Layton, R. L., Brandt, P. D., O'Connell, A. B., Hall, J. D., Freeman, A. M., … Brennwald, P. J. (2017). An evidence-based evaluation of transferrable skills and job satisfaction for science PhDs. PloS one, 12(9), e0185023. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Soler, M. C. (2025). STEM Graduate Education: Trends and Existing Interventions to Broaden STEM Graduate Pathways. St. Clair, R., Hutto, T., MacBeth, C., Newstetter, W., McCarty, N. A., & Melkers, J. (2017). The “new normal”: Adapting doctoral trainee career preparation for broad career paths in science. PloS One, 12(5), e0177035. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Stephan, P. (2012). How economics shapes science. Cambridge, MA: Harvard University Press. [ Google Scholar ] Stephan, P. (2013). How to exploit postdocs. BioScience, 63(4), 245–246. [ Google Scholar ] Subramanian, S., Hutchins, J. A., & Lundsteen, N. (2022). Bridging the gap: Increasing collaboration between research mentors and career development educators for PhD and postdoctoral training success. Molecular Biology of the Cell, 33(2), pe1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Thiry, H., Laursen, S. L., & Loshbaugh, H. G. (2015). “How do I get from here to there?” An examination of Ph. D. science students’ career preparation and decision making. International Journal of Doctoral Studies, 10, 237. [ Google Scholar ] Tokarčíková, E., Malichova, E., Kucharčíková, A., & Durišová, M. (2020). Importance of technical and business skills for future IT professionals. Amfiteatru Economic, 22(54), 567–578. [ Google Scholar ] Tomlinson-Clarke, S. M., Chaudhary, S., & Gailliard, B. M. (2019). Exploring Personal and Contextual Factors Influencing Career Choice Among Biomedical PhD Students and Post-Doctoral Fellows. Understanding Interventions, 10(1), 1–32. [ Google Scholar ] Turk-Bicakci, L., Berger, A., & Haxton, C. (2014). The nonacademic careers of STEM PhD holders. STEM at American Institutes for Research. Twiton, E. M. (2020). Faculty advisors' development of academic advising knowledge and competencies. [Doctoral dissertation, St. Cloud State University]. St. Cloud, MN: St. Cloud State University. [ Google Scholar ] Waaijer, C. J., Teelken, C., Wouters, P. F., & van der Weijden, I. C. (2018). Competition in science: Links between publication pressure, grant pressure and the academic job market. Higher Education Policy, 31, 225–243. [ Google Scholar ] Wang, C., Lo, Y. Y., Xu, Y., Wang, Y., & Porfeli, E. (2007). Constructing the search for a job in academia from the perspectives of self-regulated learning strategies and social cognitive career theory. Journal of Vocational Behavior, 70(3), 574–589. [ Google Scholar ] Wang, D., Liu, X., & Deng, H. (2022). The perspectives of social cognitive career theory approach in current times. Frontiers in psychology, 13, 1023994. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wofford, A. M., Griffin, K. A., & Roksa, J. (2021). Unequal expectations: First-generation and continuing-generation students’ anticipated relationships with doctoral advisors in STEM. Higher Education, 82(5), 1013–1029. [ Google Scholar ] Wood, C. V., Jones, R. F., Remich, R. G., Caliendo, A. E., Langford, N. C., Keller, J. L., … McGee, R. (2020). The National Longitudinal Study of Young Life Scientists: Career differentiation among a diverse group of biomedical PhD students. Plos One, 15(6), e0234259. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Woolston, C. (2021). Stagnating salaries present hurdles to career satisfaction. Nature, 599(7885), 519–521. [ Google Scholar ] Wolyniak, M. J. (2003). Balancing teaching and research experiences in doctoral training programs: Lessons for the future educator. Cell Biology Education, 2(4), 228–232. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yadav, A., & Seals, C. (2019). Taking the next step: Supporting postdocs to develop an independent path in academia. International Journal of STEM Education, 6, 1–11. [ Google Scholar ] Young, J. Q., Sugarman, R., Schwartz, J., Thakker, K., & O'Sullivan, P. S. (2020). Exploring residents’ experience of career development scholarship tracks: A qualitative case study using social cognitive career theory. Teaching and Learning in Medicine, 32(5), 522–530. [ DOI ] [ PubMed ] [ Google Scholar ] Zimmerman, A. M. (2018). Navigating the path to a biomedical science career. PloS One, 13(9), e0203783. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials cbe-25-ar25-s001.pdf (233.8KB, pdf) cbe-25-ar25-s001.pdf (233.8KB, pdf) Articles from CBE Life Sciences Education are provided here courtesy of American Society for Cell Biology ACTIONS View on publisher site PDF (380.2 KB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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