D.4.
Specific Aim 4. Establish innovative programs and align institutional policies to provide support for and ensure the success of trainees working on interdisciplinary teams. Barrier to be addressed. We have found that there are both individual and institutional barriers to the participation in and conduct of team-oriented science. These are particularly challenging for young investigators being asked work in a team-oriented fashion just when they are embarking upon their academic careers. This is further complicated because our training programs do not incorporate principles of team building or leadership. Finally, institutional policies often do not recognize individual contributions to team oriented research. D.4.1. Achieving Specific Aim 4. The ETCD Core will foster the development of the leadership skills critical for interdisciplinary team science;identify team coaches drawn from a variety of disciplines arid employ them to increase the success of the interdisciplinary teams;develop, implement, and disseminate promotion and tenure policies that recognize and reward individual contributions to interdisciplinary team science;and minimize the current financial disincentives to developing and submitting R-type awards. Rationale. Achieving all three goals of the TraCS Institute as well as the goals of the NIH Roadmap depends upon the formation and efficient function of interdisciplinary teams. Bringing together scientists from different disciplines to address a common problem creates a synergy that cannot be attained otherwise. The ETCD Core recognizes that the success of our training and career development programs will be judged by how well our trainees adopt the principles of interdisciplinary team research in order to create true changes in health practices. However, we also recognize that shifting to a team-oriented approach may be challenging for junior investigators, particularly those who are defining their own career trajectories within a system that has tended to reward individual achievements. In addition, training in effective team building is rarely included or prioritized in current training programs. Below we describe a series of programs and activities designed to address the individual and institutional barriers to successful careers in clinical and translational research.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Linked Training Award (TL1)
Project #
5TL1RR025745-04
Application #
8094464
Study Section
Special Emphasis Panel (ZRR1-SRC (99))
Program Officer
Wilde, David B
Project Start
2008-05-19
Project End
2013-04-30
Budget Start
2011-05-01
Budget End
2012-04-30
Support Year
4
Fiscal Year
2011
Total Cost
$171,422
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Zhou, Hua; Li, Lexin; Zhu, Hongtu (2013) Tensor Regression with Applications in Neuroimaging Data Analysis. J Am Stat Assoc 108:540-552
Yuan, Ying; Zhu, Hongtu; Styner, Martin et al. (2013) VARYING COEFFICIENT MODEL FOR MODELING DIFFUSION TENSORS ALONG WHITE MATTER TRACTS. Ann Appl Stat 7:102-125
Green, Robert C; Berg, Jonathan S; Grody, Wayne W et al. (2013) ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 15:565-74
Wang, Jiaping; Zhu, Hongtu; Fan, Jianqing et al. (2013) MULTISCALE ADAPTIVE SMOOTHING MODELS FOR THE HEMODYNAMIC RESPONSE FUNCTION IN FMRI. Ann Appl Stat 7:904-935
Miranda, Michelle F; Zhu, Hongtu; Ibrahim, Joseph G (2013) Bayesian spatial transformation models with applications in neuroimaging data. Biometrics 69:1074-83
Zhu, Hongtu; Ibrahim, Joseph G; Cho, Hyunsoon (2012) PERTURBATION AND SCALED COOK'S DISTANCE. Ann Stat 40:785-811
Yuan, Ying; Zhu, Hongtu; Lin, Weili et al. (2012) Local Polynomial Regression for Symmetric Positive Definite Matrices. J R Stat Soc Series B Stat Methodol 74:697-719
Guo, Ruixin; Zhu, Hongtu; Chow, Sy-Miin et al. (2012) Bayesian lasso for semiparametric structural equation models. Biometrics 68:567-77
Zhu, Hongtu; Li, Runze; Kong, Linglong (2012) MULTIVARIATE VARYING COEFFICIENT MODEL FOR FUNCTIONAL RESPONSES. Ann Stat 40:2634-2666
Zhu, Hongtu; Ibrahim, Joseph G; Cho, Hyunsoon et al. (2012) Bayesian Case Influence Measures for Statistical Models with Missing Data. J Comput Graph Stat 21:253-271

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