ArticleAEM education and training2024
What the experts see: A qualitative analysis of the behaviors of master adaptive learners in emergency medicine.
Article in AEM education and training, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Declining Performance on the Qualifying Examination: Modeling of a Potential Inflection Point in Emergency Medicine.Journal of the American College of Emergency Physicians open · 2026Article
- Precision education in medicine: A necessary transformation to better prepare physicians to meet the needs of their patients.AEM education and training · 2024Article
- What the experts see: A qualitative analysis of the behaviors of master adaptive learners in emergency medicine.AEM education and training · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The Master Adaptive Learner (MAL) model postulates that learners develop adaptive expertise through cycles of self-regulated learning. Despite a robust theoretical basis, the actual observable behaviors of MALs are not well characterized. We sought to define behaviors that characterize MALs within emergency medicine (EM) training. Methods: Using a constructivist grounded theory approach, we analyzed semistructured interviews with expert EM educators. These experts reflected on observable behaviors as well as factors in the clinical learning environment that may modulate these behaviors. We recruited using purposive sampling until thematic saturation. Results: We identified four overall themes, of which three described groups of learner behaviors and a fourth described modifiers of these behaviors. Learner behaviors include: (1) critical interrogation of practice, (2) intellectual risk-taking, and (3) intentional curation of a learning network. Critical interrogation of practice encompasses several observable behaviors including learner-driven feedback conversations, independent synthesis of clinical information, appropriate deviation from algorithms based on their conceptual understanding of core principles, intentional use of case variation and hypothetical questioning, and continuous refinement of decisions. MALs also engage in intellectual risk-taking for their development by communicating clinical decision-making processes even at the risk of being wrong, openly addressing errors and gaps, and intentionally seeking out uncomfortable experiences. Intentional curation of a learning network is the deliberate development of a consortium of trusted individuals who serve as mentors and sounding boards. We also identified a fourth theme related to the expression of learner behaviors: learning environment modulates behaviors. Active promotion of psychological safety is necessary for learners to express these behaviors. This safety is mediated through trusting relationships and expert supervisors who serve as colearners and role models. Conclusions: We present several behaviors that allow identification of MALs among EM trainees. These data expand our understanding of MALs and the critical influence of the learning environment. Identification of these behaviors may allow for more precise categorization of targeted curricular interventions and meaningful learning outcomes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.