
AbstractThis manuscript describes an experiential learning program for future collaborative biostatisticians (CBs) developed within an academic medical program. The program is a collaborative effort between the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the Master of Biostatistics (MB) programme, both housed in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and supported in partnership with the Duke Clinical and Translational Science Institute. To date, the BERD Core Training and Internship Program (BCTIP) has formally trained over 80 students to work on collaborative teams that are integrated throughout the Duke School of Medicine. This manuscript focuses on the setting for the training program, the experiential learning model on which it is based, the structure of the program, and lessons learned to date.
Special Issue Articles, experiential learning, collaborative biostatistician, internship model, collaboration and consultation, Graph representations (geometric and intersection representations, etc.), training biostatisticians
Special Issue Articles, experiential learning, collaborative biostatistician, internship model, collaboration and consultation, Graph representations (geometric and intersection representations, etc.), training biostatisticians
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
