On SAGE Insight: Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail

From Administrative Science Quarterly

We have known for decades that the world of work is changing and that communities of practice must cultivate new skills to stay relevant. New business models, collaborative practices and technologies all demand new ways of interpreting and acting skillfully in the midst of a new and shifting set of problems, and communities that do not adapt to these conditions deliver less value and lose their jurisdiction. This research draws on two interleaved studies: a multi-sited ethnography and an interview-based study. The paper explores how trainees in a community of practice learn new techniques and technologies when approved practices for learning are insufficient. This study encompassed two surgical methodologies, open and robotic, each with distinct technological accoutrements. By studying medical residents trying to learn robotic surgical technique, the author found that trainees who successfully learn under these conditions do so via what is called shadow learning. Shadow learning practices were frequently tolerated by key stakeholders, because they allowed for mutually desirable outcomes, even though they might have been punished or forbidden if viewed in the light of day. The theoretical contribution of the author is to show how trainees learn skills core to a community of practice through such methods. The study offers strong evidence that efficiency and liability pressures may win the day at the expense of maintaining legitimate peripheral participation in skilled work.

Abstract

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’ role in the work, making approved methods ineffective. Learning surgery in this context required what I call “shadow learning”: 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: “premature specialization” in robotic surgical technique at the expense of generalist training; “abstract rehearsal” before and during their surgical rotations when concrete, empirically faithful rehearsal was prized; and “undersupervised struggle,” in which they performed robotic surgical work close to the edge of their capacity with little expert supervision—when 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.

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Article details
Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail
Matthew Beane
First Published January 9, 2018
DOI: 10.1177/0001839217751692

Administrative Science Quarterly


     
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