Shadow learning: building robotic surgical skill when approved means fail - Beane, Matthew (2019)
I explore here how trainees in a community of practice learn new techniques and technologies when approved practices for learning are insufficient. I do so through two studies: a two-year, five-sited, comparative ethnographic study of learning in robotic and traditional surgical practice, and a blinded interview-based study of surgical learning practices at 13 top-tier teaching hospitals around the U.S. I found that learning surgery through increasing participation using approved methods worked well in traditional (open) surgery, as current literature would predict. But the radically different practice of robotic surgery greatly limited trainees$1 (Brole in the work, making approved methods ineffective. Learning surgery in this context required what I call (3z(Bshadow learning(3y(B: an interconnected set of norm- and policy-challenging practices enacted extensively, opportunistically, and in relative isolation that allowed only a minority of robotic surgical trainees to come to competence. Successful trainees engaged extensively in three practices: (3z(Bpremature specialization(3y (Bin robotic surgical technique at the expense of generalist training; (3z(Babstract rehearsal(3y (Bbefore and during their surgical rotations when concrete, empirically faithful rehearsal was prized; and (3z(Bundersupervised struggle,(3y (Bin which they performed robotic surgical work close to the edge of their capacity with little expert supervision$1 (Bwhen norms and policy dictated such supervision. Shadow learning practices were neither punished nor forbidden, and they contributed to significant and troubling outcomes for the cadre of initiate surgeons and the profession, including hyperspecialization and a decreasing supply of experts relative to demand. - Reproduced.s
Keywords: Learning methods; Information technology; Robotics