PhD recipients acquire discipline-specific knowledge and a range of relevant skills during their training in the life sciences, physical sciences, computational sciences, social sciences, and engineering. Empirically testing the applicability of these skills to various careers held by graduates will help assess the value of current training models. This report details results of an Internet survey of science PhDs (n = 8099) who provided ratings for fifteen transferrable skills. Indeed, analyses indicated that doctoral training develops these transferrable skills, crucial to success in a wide range of careers including research-intensive (RI) and non-research-intensive (NRI) careers. Notably, the vast majority of skills were transferrable across both RI and NRI careers, with the exception of three skills that favored RI careers (creativity/innovative thinking, career planning and awareness skills, and ability to work with people outside the organization) and three skills that favored NRI careers (time management, ability to learn quickly, ability to manage a project). High overall rankings suggested that graduate training imparted transferrable skills broadly. Nonetheless, we identified gaps between career skills needed and skills developed in PhD training that suggest potential areas for improvement in graduate training. Therefore, we suggest that a two-pronged approach is crucial to maximizing existing career opportunities for PhDs and developing a career-conscious training model: 1) encouraging trainees to recognize their existing individual skill sets, and 2) increasing resources and programmatic interventions at the institutional level to address skill gaps. Lastly, comparison of job satisfaction ratings between PhD-trained employees in both career categories indicated that those in NRI career paths were just as satisfied in their work as their RI counterparts. We conclude that PhD training prepares graduates for a broad range of satisfying careers, potentially more than trainees and program leaders currently appreciate.
The relative importance of reasons for current career choices for science, technology, engineering, and mathematics PhDs was examined. Reasons why underrepresented minority scientists chose faculty careers differed in some respects from those of well-represented scientists, with implications for graduate/postdoctoral training, formal and informal social support networks, and faculty career decisions.
Repetitive negative thinking (RNT) is a transdiagnostic process involved in the risk, maintenance, and relapse of serious conditions including mood disorders, anxiety, eating disorders, and addictions. Processing mode theory provides a theoretical model to assess, research, and treat RNT using a transdiagnostic approach. Clinical researchers also often employ categorical approaches to RNT, including a focus on depressive rumination or worry, for similar purposes. Three widely used self-report questionnaires have been developed to assess these related constructs: the Ruminative Response Scale (RRS), the Perseverative Thinking Questionnaire (PTQ), and the Mini-Cambridge Exeter Repetitive Thought Scale (Mini-CERTS). Yet these scales have not previously been used in conjunction, despite useful theoretical distinctions only available in Mini-CERTS. The present validation of the methods in a Polish speaking population provides psychometric parameters estimates that contribute to current efforts to increase reliable replication of theoretical outcomes. Moreover, the following study aims to present particular characteristics and a comparison of the three methods. Although there has been some exploration of a categorical approach, the comparison of transdiagnostic methods is still lacking. These methods are particularly relevant for developing and evaluating theoretically based interventions like concreteness training, an emerging field of increasing interest, which can be used to address the maladaptive processing mode in RNT that can lead to depression and other disorders. Furthermore, the translation of these measures enables the examination of possible cross-cultural structural differences that may lead to important theoretical progress in the measurement and classification of RNT. The results support the theoretical hypothesis. As expected, the dimensions of brooding, general repetitive negative thinking, as well as abstract analytical thinking, can all be classified as unconstructive repetitive thinking. The particular characteristics of each scale and potential practical applications in clinical and research are discussed.
Four types of experiential learning approaches used for predoctoral graduate students and postdoctoral scholars in the biomedical sciences are described and associated learning objectives and evaluation strategies are compared. This framework will help other institutions design and deliver experiential learning programs for career training.
Background: There has been a groundswell of national support for transparent tracking and dissemination of PhD career outcomes. In 2017, individuals from multiple institutions and professional organizations met to create the Unified Career Outcomes Taxonomy (UCOT 2017), a three-tiered taxonomy to help institutions uniformly classify career outcomes of PhD graduates. Early adopters of UCOT 2017, noted ambiguity in some categories of the career taxonomy, raising questions about its consistent application within and across institutions. Methods: To test and evaluate the consistency of UCOT 2017, we calculated inter-rater reliability across two rounds of iterative refinement of the career taxonomy, classifying over 800 PhD alumni records via nine coders. Results: We identified areas of discordance in the taxonomy, and progressively refined UCOT 2017 and an accompanying Guidance Document to improve inter-rater reliability across all three tiers of the career taxonomy. However, differing interpretations of the classifications, especially for faculty classifications in the third tier, resulted in continued discordance among the coders. We addressed this discordance with clarifying language in the Guidance Document, and proposed the addition of a flag system for identification of the title, rank, and prefix of faculty members. This labeling system provides the additional benefit of highlighting the granularity and the intersectionality of faculty job functions, while maintaining the ability to sort by - and report data on - faculty and postdoctoral trainee roles, as is required by some national and federal reporting guidelines. We provide specific crosswalk guidance for how a user may choose to incorporate our suggestions while maintaining the ability to report in accordance with UCOT 2017. Conclusions: Our findings underscore the importance of detailed guidance documents, coder training, and periodic collaborative review of career outcomes taxonomies as PhD careers evolve in the global workforce. Implications for coder-training and use of novice coders are also discussed.
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