Village mentoring and hive learning: The MIT Critical Data experience

• Type of Material: Article
• Authors: Christopher V Cosgriff, Hospital of the University of Pennsylvania; Marie Charpignon, Massachusetts Institute of Technology; Dana Moukheiber, University at Buffalo, The State University of New York; Mary E Lough, Stanford University; Judy Gichoya, Emory University; David J Stone, University of Virginia; Leo Anthony Celi, Massachusetts Institute of Technology
• Language: English
• Date: 2021-06-25
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Author(s)
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.isci.2021.102656
• Title of Journal or Parent Work: iScience
• Volume: 24
• Issue: 6
• Keywords: MIT Critical Data
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

It is often said that every system is perfectly designed to get the results it achieves. This invocation challenges us to rethink the systems we have in order to produce the results that we want. Consider then our current biomedical research apparatus and how its training and funding processes foster the crisis of replication, the insular anti-collaborative nature of closed data, and a system that preserves existing power structures and rewards narcissism. The COVID-19 pandemic has further highlighted many of the weaknesses of our current approach. A key feature of the current system is the structure of research groups which may be conceptualized as mentorship hierarchies led by principal investigators. While intergroup collaboration is common, the structure rewards competition between groups and incentivizes selfishness: trainees developing within this structure are compelled to become hyperspecialized in hopes of obtaining grant funding in a unique area and developing into principal investigators with groups of their own.