Research-Doctorate Programs in the Biomedical Sciences Selected Findings from the NRC Assessment (2011) / Chapter Skim
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asked the NRC to examine data on the biomedical sciences programs to see if they could shed light on specific questions about research training and support, many of which were highlighted in Investing In the Future, National Institute of General Medical Sciences Strategic Plan for Biomedical and Behavioral Research Training2. Given its substantial investment in doctoral research training, NIH was particularly interested in the following questions: 1)
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TABLE S-1 Fields in the Biomedical Sciences in the Assessment of Research Doctorate Programs and Number of Programs Included in Each Field Number of Field Name Programs Biochemistry, Biophysics, and Structural Biology 157 Biomedical Engineering and Bioengineering 74 Cell and Developmental Biology 120 Genetics and Genomics 66 Immunology and Infectious Disease 68 Integrated Biological and Biomedical Sciences 113 Microbiology 71 Neuroscience and Neurobiology 93 Nutrition 45 Pharmacology, Toxicology, and Environmental Health 117 Physiology 58 METHODOLOGY At the outset, it is important for the reader to understand the sources and some of the limitations of the data used to produce the correlations and other descriptions in this report. The committee authoring the Assessment identified several sources of errors in the data that could not be eliminated, including classification errors and data collection errors (see Box 2-1)
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2a) Correlation of Median Time to Degree or Completion Rates with Faculty Research Productivity The panel found correlations greater than or equal to 0.3 in six fields between the average student time to degree and various measures of faculty research productivity: publications per faculty member, citations per publication, and the percent of faculty with grants.
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TABLE S-3 Fields with Correlations > 0.3 Between Median Time to Degree or Completion Rates and Average GRE Scores or Average Number of Ph.D.'s Field Correlation > 0.3 Biomedical Engineering and Bioengineering Median Time to Degree with Average Number of Ph.D.'s Microbiology Median Time to Degree with Average GRE Scores Nutrition Median Time to Degree with Average Number of Ph.D.'s Nutrition Median Time to Degree with Average GRE Scores Physiology Completion Rate with Average Number of Ph.D.'s 2c) Correlation of Median Time to Degree with Student Activities The panel did not conduct an analysis of the possible correlations between median time to degree and student activities such as writing workshops and career seminars.
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faculty and URM students in six of the eleven biomedical science fields: TABLE S-4 Fields with Correlations > 0.3 Between Percent of Underrepresented Minority (URM) Faculty and Percent of URM Students Correlation of % URM Faculty with % URM Students Field Biochemistry, Biophysics, and Structural Biology 0.489 Integrated Biological and Biomedical Sciences 0.529 Microbiology 0.765 Nutrition 0.531 Pharmacology, Toxicology, and Environmental Health 0.370 Physiology 0.570 Potential factors associated with increased URM student enrollment are explored in Chapter 5.
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Rate (%) Correlation Biochemistry, Biophysics, and Structural Biology 5.63 45.9 -0.375 Biomedical Engineering and Bioengineering 5.06 46.3 -0.134 Cell and Developmental Biology 5.66 50.1 -0.383 Genetics and Genomics 5.73 41.6 -0.451 Immunology and Infectious Disease 5.36 56.2 -0.071 Integrated Biological and Biomedical Sciences 5.62 47.4 -0.362 Microbiology 5.58 47.1 -0.493 Neuroscience and Neurobiology 5.68 46.2 -0.464 Nutrition 4.88 55.8 -0.165 Pharmacology, Toxicology, and Environmental Health 5.21 56.1 -0.260 Physiology 5.13 50.9 -0.179 A Deeper Analysis of Underrepresented Minorities The Role of Training Grants Associating NIH training grants with the university to which each program belongs, the panel investigated two questions about the relationship of training grant awards to underrepresented minority (URM)
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Thus, having more training grants does not appear to increase the fraction of minority students or international students. A Statistical Approach to Factors Associated with URM Enrollment Simple correlations cannot tell the whole story, and the panel also developed a statistical model that relates enrollment by URM's to other program characteristics, in order to better understand how to expand URM enrollment and graduation from PhD programs.
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programs in the Assessment, the panel also investigated whether there are unmeasured characteristics of each university that attract URM Ph.D. students beyond the predictors considered above, i.e., whether URM students cluster in biomedical science programs at a given university due to a random "university effect" that is common to all the programs within that institution.
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