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Managing cognitive load and enhancing metacognitive learning in postgraduate training and practice

  • Isaac K.S. Ng EMAIL logo , Christine J. Ko and Tow Keang Lim
Published/Copyright: August 13, 2025
Diagnosis
From the journal Diagnosis

Abstract

The phase of postgraduate medical training and practice is notoriously difficult because junior physicians or medical residents find themselves stuck in a tenuous situation of having to handle newfound heavy clinical work and responsibilities while scaling a steep learning curve on the job. In recent years, increased focus on diagnostic error has led to increasing calls to re-evaluate how clinical reasoning is cultivated in medical training, with emphasis on pedagogical interventions that aim to sharpen clinical judgments while minimising cognitive errors. Against this backdrop, we herein review the concept of “cognitive load” in post-graduate training and clinical practice, and discuss its relevance to effective metacognitive learning amidst clinical duties and to optimisation of medical decision-making in real-world settings by reducing cognitive errors in the form of bias and noise. We then outline pedagogical and workplace-based interventions that may target the twin problem of intrinsic and extrinsic cognitive load in clinical learning and work, and specifically advocate metacognitive-based practices that promote iterative cycles of cognitive schema re-calibration and professional development.


Corresponding author: Dr Isaac K.S. Ng, MBBS, MRCP (UK), Division of Rheumatology, Department of Medicine, National University Hospital, 1E Kent Ridge Road, 119228, Singapore, Singapore, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: IKSN wrote the manuscript draft. IKSN and TKL conceived the study idea. CJK and TKL critically reviewed and edited the manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-05-09
Accepted: 2025-07-23
Published Online: 2025-08-13

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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