The Best Laid Plans? A Qualitative Investigation of How Resident Physicians Plan Their Learning

Purpose Adaptive expertise (AE) has been identified as a critical trait to cultivate in future physicians. The 4-phase master adaptive learner (MAL) conceptual model describes the learning skills and behaviors necessary to develop AE. Though prior work has elucidated skills and behaviors used by MALs in the initial planning phase of learning, most resident learners are not thought to be MALs. In this study, the authors investigated how these majority "typical" learners develop AE by exploring the strategies they used in the planning phase of learning. Method Participants were resident physicians at graduate medical education (GME) training programs located at 4 academic medical centers in the United States. Participants participated in semistructured individual interviews in 2021, and interview transcripts were analyzed using constant comparative analysis of grounded theory. Results Fourteen subjects representing 8 specialties were interviewed, generating 152 pages of transcripts for analysis. Three themes were identified: "Typical" learners were challenged by the transition from structured undergraduate medical education learning to less-structured GME learning, lacked necessary skills to easily navigate this transition, and relied on trial and error to develop their learning skills. Conclusions Participants used trial and error to find learning strategies to help them manage the systemic challenges encountered when transitioning from medical school to residency. The success (or failure) of these efforts was tied to learners' efficacy with the self-regulated learning concepts of agency, metacognitive goal setting, and motivation. A conceptual model is provided to describe the impact of these factors on residents' ability to be adaptive learners, and actionable recommendations are provided to help educators' efforts to foster adaptive learning skills and behaviors. These findings also provided valuable evidence of validity of the MAL model that has thus far been lacking.