The Association of Master Adaptive Learning With Less Burnout and More Resilience in Obstetrics and Gynecology
Purpose:
Medical education should prepare learners for complex and evolving work, and should ideally include the Master Adaptive Learner (MAL) model-meta-learning skills for continuous self-regulated learning. This study aimed to measure obstetrics and gynecology (OB/GYN) residents' MAL attributes, assess associations with burnout and resilience, and explore learning task associations with MAL.
Method:
OB/GYN residents were surveyed electronically at an in-training examination in January 2022. The survey included demographic information, the 2-item Maslach Burnout Inventory, the 2-item Connor-Davidson Resilience Scale, 4 MAL items (e.g., "I take every opportunity to learn new things"), and questions about training and learning experiences.
Results:
Of 5,761 residents, 3,741 respondents (65%) were included. A total of 1,478 of 3,386 (39%) demonstrated burnout (responded positive for burnout on emotional exhaustion or depersonalization items). The mean (SD) Connor-Davidson Resilience Scale score was 6.4 (1.2) of a total possible score of 8. The mean (SD) MAL score was 16.3 (2.8) of a total possible score of 20. The MAL score was inversely associated with burnout, with lower MAL scores for residents with (mean [SD] MAL score, 16.5 [2.4]) vs without (mean [SD], 16.0 [2.3]) burnout ( P < .001). Higher MAL scores were associated with higher resilience ( R = 0.29, P < .001). Higher MAL scores were associated with the statement "I feel that I was well prepared for my first year of residency" ( R = 0.19, P < .001) and a plan to complete subspecialty training after residency (mean [SD] of 16.6 [2.4] for "yes" and 16.2 [2.4] for "no," P < .001).
Conclusions:
Residents who scored higher on MAL showed more resilience and less burnout. Whether less resilient, burned-out residents did not have the agency to achieve MAL status or whether MAL behaviors filled the resiliency reservoir and protected against burnout is not clear.
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