Concept: Academic degree
EDITORIAL: David Pimentel is a professor of ecology and agricultural sciences at Cornell University, Ithaca, NY 14853-0901. His Ph.D. is from Cornell University and had postdoctoral research at the University of Chicago, MIT, and fellowship at Oxford University (England). He was awarded a distinguished honorary degree from the University of Massachusetts. His research spans the fields of energy, population ecology, biological pest control, pesticides, sustainable agriculture, land and water conservation, livestock, and environmental policy. Pimentel has published more than 700 scientific papers and 37 books and has served on many national and government committees including the National Academy of Sciences; President’s Science Advisory Council; U.S Department of Agriculture; U.S. Department of Energy; U.S. Department of Health, Education and Welfare; Office of Technology Assessment of the U.S. Congress; and the U.S. State Department. He is currently Editorial Advisor for BMC Ecology. In this article, he reflects on 50 years since the publication of Rachel Carson’s influential book, Silent Spring.
We introduce a new method for detecting communities of arbitrary size in an undirected weighted network. Our approach is based on tracing the path of closest-friendship between nodes in the network using the recently proposed Generalized Erds Numbers. This method does not require the choice of any arbitrary parameters or null models, and does not suffer from a system-size resolution limit. Our closest-friend community detection is able to accurately reconstruct the true network structure for a large number of real world and artificial benchmarks, and can be adapted to study the multi-level structure of hierarchical communities as well. We also use the closeness between nodes to develop a degree of robustness for each node, which can assess how robustly that node is assigned to its community. To test the efficacy of these methods, we deploy them on a variety of well known benchmarks, a hierarchal structured artificial benchmark with a known community and robustness structure, as well as real-world networks of coauthorships between the faculty at a major university and the network of citations of articles published in Physical Review. In all cases, microcommunities, hierarchy of the communities, and variable node robustness are all observed, providing insights into the structure of the network.
The U.S. National Institutes of Health (NIH) budget expansion from 1998 through 2003 increased demand for biomedical research, raising relative wages and total employment in the market for biomedical scientists. However, because research doctorates in biomedical sciences can often take six years or more to complete, the full labor supply response to such changes in market conditions is not immediate, but rather is observed over a period of several years. Economic rational expectations models assume that prospective students anticipate these future changes, and also that students take into account the opportunity costs of their pursuing graduate training. Prior empirical research on student enrollment and degree completions in science and engineering (S&E) fields indicates that “cobweb” expectations prevail: that is, at least in theory, prospective graduate students respond to contemporaneous changes in market wages and employment, but do not forecast further changes that will arise by the time they complete their degrees and enter the labor market. In this article, we analyze time-series data on wages and employment of biomedical scientists versus alternative careers, on completions of S&E bachelor’s degrees and biomedical sciences PhDs, and on research expenditures funded both by NIH and by biopharmaceutical firms, to examine the responsiveness of the biomedical sciences labor supply to changes in market conditions. Consistent with previous studies, we find that enrollments and completions in biomedical sciences PhD programs are responsive to market conditions at the time of students' enrollment. More striking, however, is the close correspondence between graduate student enrollments and completions, and changes in availability of NIH-funded traineeships, fellowships, and research assistantships.
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.
There is a persistent shortage of underrepresented minority (URM) faculty who are involved in basic biomedical research at medical schools. We examined the entire training pathway of potential candidates to identify the points of greatest loss. Using a range of recent national data sources, including the National Science Foundation’s Survey of Earned Doctorates and Survey of Doctoral Recipients, we analyzed the demographics of the population of interest, specifically those from URM backgrounds with an interest in biomedical sciences. We examined the URM population from high school graduates through undergraduate, graduate, and postdoctoral training as well as the URM population in basic science tenure track faculty positions at medical schools. We find that URM and non-URM trainees are equally likely to transition into doctoral programs, to receive their doctoral degree, and to secure a postdoctoral position. However, the analysis reveals that the diversions from developing a faculty career are found primarily at two clearly identifiable places, specifically during undergraduate education and in transition from postdoctoral fellowship to tenure track faculty in the basic sciences at medical schools. We suggest focusing additional interventions on these two stages along the educational pathway.
Undergraduate Students' interest in taking quantitative vs. non quantitative courses has received limited attention even though it has important consequences for higher education. Previous studies have collected course interest ratings at the end of the courses as part of student evaluation of teaching (SET) ratings, which may confound prior interest in taking these courses with students' actual experience in taking them. This study is the first to examine undergraduate students' interest in quantitative vs. non quantitative courses in their first year of studies before they have taken any quantitative courses. Three hundred and forty students were presented with descriptions of 44 psychology courses and asked to rate their interest in taking each course. Student interest in taking quantitative vs non quantitative courses was very low; the mean interest in statistics courses was nearly 6 SDs below the mean interest in non quantitative courses. Moreover, women were less interested in taking quantitative courses than men. Our findings have several far-reaching implications. First, evaluating professors teaching quantitative vs. non quantitative courses against the same SET standard may be inappropriate. Second, if the same SET standard is used for the evaluation of faculty teaching quantitative vs. non quantitative courses, faculty are likely to teach to SETs rather than focus on student learning. Third, universities interested primarily in student satisfaction may want to expunge quantitative courses from their curricula. In contrast, universities interested in student learning may want to abandon SETs as a primary measure of faculty teaching effectiveness. Fourth, undergraduate students who are not interested in taking quantitative courses are unlikely to pursue graduate studies in quantitative psychology and unlikely to be able to competently analyze data independently.
To examine clinical doctoral students' demographic and training characteristics, career intentions, career preparedness and what influences them as they plan their future careers.
Scientific Dishonesty: A Survey of Doctoral Students at the Major Medical Faculties in Sweden and Norway
- Journal of empirical research on human research ethics : JERHRE
- Published almost 3 years ago
As we need knowledge about the prevalence of scientific dishonesty, this study investigates the knowledge of, experiences with, and attitudes toward various forms of scientific dishonesty among PhD students at the main medical faculties in Sweden and Norway. An anonymous questionnaire was distributed to all post-graduate research students attending basic PhD courses at the medical faculties in Stockholm and Oslo during the fall 2014. The responding doctoral students reported to know about various forms of scientific dishonesty from the literature, in their department, and for some also through their own experience. Some forms of scientific misconduct were considered to be acceptable by a significant minority. There was a high level of willingness to report misconduct but little awareness of relevant policies for scientific conduct.
Exploding head syndrome is characterized by the perception of loud noises during sleep-wake or wake-sleep transitions. Although episodes by themselves are relatively harmless, it is a frightening phenomenon that may result in clinical consequences. At present there are little systematic data on exploding head syndrome, and prevalence rates are unknown. It has been hypothesized to be rare and to occur primarily in older (i.e. 50+ years) individuals, females, and those suffering from isolated sleep paralysis. In order to test these hypotheses, 211 undergraduate students were assessed for both exploding head syndrome and isolated sleep paralysis using semi-structured diagnostic interviews: 18.00% of the sample experienced lifetime exploding head syndrome, this reduced to 16.60% for recurrent cases. Though not more common in females, it was found in 36.89% of those diagnosed with isolated sleep paralysis. Exploding head syndrome episodes were accompanied by clinically significant levels of fear, and a minority (2.80%) experienced it to such a degree that it was associated with clinically significant distress and/or impairment. Contrary to some earlier theorizing, exploding head syndrome was found to be a relatively common experience in younger individuals. Given the potential clinical impacts, it is recommended that it be assessed more regularly in research and clinical settings.
Attending school is a multifaceted experience. Students are not only exposed to new knowledge but are also immersed in a structured environment in which they need to respond flexibly in accordance with changing task goals, keep relevant information in mind, and constantly tackle novel problems. To quantify the cumulative effect of this experience, we examined retrospectively and prospectively, the relationships between educational attainment and both cognitive performance and learning. We analyzed data from 196,388 subscribers to an online cognitive training program. These subscribers, ages 15-60, had completed eight behavioral assessments of executive functioning and reasoning at least once. Controlling for multiple demographic and engagement variables, we found that higher levels of education predicted better performance across the full age range, and modulated performance in some cognitive domains more than others (e.g., reasoning vs. processing speed). Differences were moderate for Bachelor’s degree vs. High School (d = 0.51), and large between Ph.D. vs. Some High School (d = 0.80). Further, the ages of peak cognitive performance for each educational category closely followed the typical range of ages at graduation. This result is consistent with a cumulative effect of recent educational experiences, as well as a decrement in performance as completion of schooling becomes more distant. To begin to characterize the directionality of the relationship between educational attainment and cognitive performance, we conducted a prospective longitudinal analysis. For a subset of 69,202 subscribers who had completed 100 days of cognitive training, we tested whether the degree of novel learning was associated with their level of education. Higher educational attainment predicted bigger gains, but the differences were small (d = 0.04-0.37). Altogether, these results point to the long-lasting trace of an effect of prior cognitive challenges but suggest that new learning opportunities can reduce performance gaps related to one’s educational history.