Residents and faculty have described a burden of assessment related to the implementation of competency-based medical education (CBME), which may undermine its benefits. Although this concerning signal has been identified, little has been done to identify adaptations to address this problem. Grounded in an analysis of an early Canadian pan-institutional CBME adopter’s experience, this article describes postgraduate programs’ adaptations related to the challenges of assessment in CBME. From June 2019–September 2022, 8 residency programs underwent a standardized Rapid Evaluation guided by the Core Components Framework (CCF). Sixty interviews and 18 focus groups were held with invested partners. Transcripts were analyzed abductively using CCF, and ideal implementation was compared with enacted implementation. These findings were then shared back with program leaders, adaptations were subsequently developed, and technical reports were generated for each program. Researchers reviewed the technical reports to identify themes related to the burden of assessment with a subsequent focus on identifying adaptations across programs. Three themes were identified: (1) disparate mental models of assessment processes in CBME, (2) challenges in workplace-based assessment processes, and (3) challenges in performance review and decision making. Theme 1 included entrustment interpretation and lack of shared mindset for performance standards. Adaptations included revising entrustment scales, faculty development, and formalizing resident membership. Theme 2 involved direct observation, timeliness of assessment completion, and feedback quality. Adaptations included alternative assessment strategies beyond entrustable professional activity forms and proactive assessment planning. Theme 3 related to resident data monitoring and competence committee decision making. Adaptations included adding resident representatives to the competence committee and assessment platform enhancements. These adaptations represent responses to the concerning signal of significant burden of assessment within CBME being experienced broadly. The authors hope other programs may learn from their institution’s experience and navigate the CBME-related assessment burden their invested partners may be facing.

Simulation is increasingly being used in postgraduate medical education as an opportunity for competency assessment. However, there is limited direct evidence that supports performance in the simulation lab as a surrogate of workplace-based clinical performance for non-procedural tasks such as resuscitation in the emergency department (ED). We sought to directly compare entrustment scoring of resident performance in the simulation environment to clinical performance in the ED. The resuscitation assessment tool (RAT) was derived from the previously implemented and studied Queen’s simulation assessment tool (QSAT) via a modified expert review process. The RAT uses an anchored global assessment scale to generate an entrustment score and narrative comments. Emergency medicine (EM) residents were assessed using the RAT on cases in simulation-based examinations and in the ED during resuscitation cases from July 2016 to June 2017. Resident mean entrustment scores were compared using Pearson’s correlation coefficient to determine the relationship between entrustment in simulation cases and in the ED. Inductive thematic analysis of written commentary was conducted to compare workplace-based with simulation-based feedback. There was a moderate, positive correlation found between mean entrustment scores in the simulated and workplace-based settings, which was statistically significant (r = 0.630, n = 17, p < 0.01). Further, qualitative analysis demonstrated overall management and leadership themes were more common narratives in the workplace, while more specific task-based feedback predominated in the simulation-based assessment. Both workplace-based and simulation-based narratives frequently commented on communication skills. In this single-center study with a limited sample size, assessment of residents using entrustment scoring in simulation settings was demonstrated to have a moderate positive correlation with assessment of resuscitation competence in the workplace. This study suggests that resuscitation performance in simulation settings may be an indicator of competence in the clinical setting. However, multiple factors contribute to this complicated and imperfect relationship. It is imperative to consider narrative comments in supporting the rationale for numerical entrustment scores in both settings and to include both simulation and workplace-based assessment in high-stakes decisions of progression.

Research has shown that taking ‘timeouts’ in medical practice improves performance and patient safety. However, the benefits of taking timeouts, or pausing, are not sufficiently acknowledged in workplaces and training programmes. To promote this acknowledgement, we suggest a systematic conceptualisation of the medical pause, focusing on its importance, processes and implementation in training programmes. By employing insights from educational and cognitive psychology, we first identified pausing as an important skill to interrupt negative momentum and bolster learning. Subsequently, we categorised constituent cognitive processes for pausing skills into two phases: the decision‐making phase (determining when and how to take pauses) and the executive phase (applying relaxation or reflection during pauses). We present a model that describes how relaxation and reflection during pauses can optimise cognitive load in performance. Several strategies to implement pause training in medical curricula are proposed: intertwining pause training with training of primary skills, providing second‐order scaffolding through shared control and employing auxiliary tools such as computer‐based simulations with a pause function.

Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant’s level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner’s capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders’ expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.