Cognitive load in clinical training: a scoping review of factors and strategies linked to well‑being, retention, and performance

Cognitive load in clinical training: a scoping review of factors and strategies linked to well‑being, retention, and performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive load in clinical training: a scoping review of factors and strategies linked to well‑being, retention, and performance Amit Bansal, Rosalie Wing Yan Lai, Linn Liljeros, Jagkirat Singh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9108201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cognitive load is the mental effort required to perform a task due to demands on working memory storage and information processing. In clinical training, the cognitive load can be high due to the task complexity. Applying cognitive load management strategies may help educators support trainees’ well-being, retention, and performance. Limited research exists on the translating cognitive load management strategies with practical recommendations for enhancing clinical trainees' retention, well-being, and performance. Objectives: This scoping review aims to identify cognitive load factors and management strategies that can be used to improve trainees’ well-being, retention, and performance in clinical training and practice. Methods: We investigated the impacts of different cognitive load factors and management strategies on trainees’ well-being, retention, and performance, through a scoping review with narrative synthesis. No meta-analysis was planned. We followed PRISMA-ScR for search and screening. Databases (Embase, MEDLINE, Web of Science, CINAHL) searched 18-19 February 2025; Google Scholar and hand‑searches updated 27 February 2026. Results: We identified 8,395 records (Embase 110; Medline 124; Web of Science 188; CINAHL 66; Google Scholar and hand searches 7,907). After deduplication, screening and full-text review, 125 studies were included. Cognitive load management strategies were clustered here as (i) optimising intrinsic load via task segmentation and part-task/step-up sequencing; (ii) minimising extraneous load via de-cluttering, supportive environments and clear and concise instructions; (iii) optimising germane load via structured feedback, worked examples, and appropriately staged simulation. Conclusions: Cognitive load management strategies and institutional support may be important for trainee retention, well-being, and performance in clinical training. By using evidence-based strategies, healthcare systems may help trainees better manage cognitive load and improve learning and performance. Educational Psychology Health Economics & Outcomes Research Cognitive load healthcare workers patient safety burnout operative performance well-being simulation deliberate practice metacognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Summary Healthcare workers face substantial cognitive load, with limited research on practical strategies to manage it and support clinical trainees' retention, well-being, and performance. Across diverse designs, higher perceived workload is often associated with poorer well‑being and intent to leave; effects on performance vary by task complexity and expertise. Managing intrinsic, extraneous, and germane load may be associated with better learning efficiency; links to well‑being and retention are suggested but heterogeneous across studies. Promising, but heterogeneously evaluated, strategies include deliberate practice, structured instructional supports (worked examples, schemas, mnemonics), situational awareness, reflection, and simulation. Organisational supports (e.g., protected time, leadership support, workload redesign) are associated with well-being and, in turn, support performance. Introduction Clinical training requires rapid information processing, multi‑tasking, and high‑stakes decision‑making conditions that can impose substantial cognitive demands on healthcare workers (HCWs). The elevated stakes inherent in healthcare settings are often associated with emotional, cognitive and physical stresses, contributing to elevated burnout 1 and suicide ideation/risk 2,3 in some cohorts. Estimates of burnout vary widely by cohort, instrument, and period; for example, a meta‑analysis reports ~ 37% among medical students 4 , while other cohorts show substantial heterogeneity (10–77%) 5–8 . Prevalences varies by cohort, instrument, and period; putative contributors include workload, responsibility, and exposure to morbidity and mortality on a regular basis. HCW training has changed dramatically due to the coronavirus disease (COVID-19) pandemic, which unfortunately leads to even higher risks of physical isolation, burnout, and suicidal thoughts for medical trainees 9 . In a cross-sectional study, personal protective equipment (PPE) availability, staff training pre-redeployment, and provision of mental health support, were significantly associated with mental health in 41 countries 10 . This is likely related to higher risk for HCWs of being infected with respiratory viruses 11 , 12 . Moreover, a 2022 paper 13 reported that intense increase in burnout (63% in 2021 vs. 38% in 2020, p < 0.001) and decrease in satisfaction with work-life balance (46% in 2020 vs. 30% in 2021, p < 0.001) occurred in US physicians. In the US 14 , 1 in 5 physicians and 2 in 5 nurses who worked in the COVID-19 response intend to leave their practice altogether in 2020. Burnout, fear of exposure, COVID-19-related anxiety/depression, and workload were all independently related to intent to reduce work hours or leave one’s practice within 1 year (all p < 0.01) 14 . Additionally, redeployment to new roles without adequate training and lack of mental health support may increase the risk of burnout and suicidal ideation in HCWs 10 . Cognitive load refers to the amount of intellectual effort required to process information or stressors due to demands on working memory storage and information processing 15 . Cognitive load theory distinguishing intrinsic (linked to the task itself), extraneous (external to the task or unnecessary for completing the task such as environment) and germane (to facilitate learning) loads 16 – 19 . New information is processed by bounded working memory capacity and duration, with learning supported by schema formation in long-term memory for future use 17 . Allostatic load is the cumulative burden of chronic stress and life events. Practical stress-management approached are often advocated for normal allostatic response 20 – 22 (Fig. 1 ); however, our review focuses on training design and system factors linked to cognitive load. Competence is the inherent state of possessing knowledge and cognitive skills to successfully perform a specific or range of tasks 23 . It is often difficult to determine, and different medical specialities have their specific criteria for evaluation. Certification (or licensing) is the method by which external organisations formally recognise a provider and provide them appropriate authorisation to manage patients, and tasks based the competence of the provider 23 . Cognitive overload occurs when the overall cognitive load surpasses the working memory capacity of trainees 24 . During training, cognitive load of HCWs can be high due to the complexity of the procedures, the amount of information that needs to be processed, and the stress of working in a high-pressure environment. Cognitive overload and low perceived value are associated with poorer well-being and training outcomes, with potential implications for patient safety 13 , 14 , 16 , 25 , 26 . Cognitive load management is described here as the proactive management of cognitive stressors to facilitate trainees’ wellness and performance across the training period. Tokuno et al. 25 implied that cognitive overload is more likely in trainees performing more complex tasks and in high-fidelity environments and is more common among less experienced trainees. While cognitive load theory offers valuable insights into learning design, existing research has primarily focused on specific educational settings or professions. This review aims to bridge this gap by examining the cognitive load factors and management strategies associated with outcomes in HCW training and practice. By exploring the interplay between cognitive load, well-being, retention, and performance in this under-researched domain, this review contributes to approaches for supporting HCWs. To reduce the likelihood of medical and surgical trainees leaving their profession and enhance their well-being and performance, it is crucial to develop strategies to help these trainees manage their cognitive load more efficiently. This review discusses cognitive overload and explores strategies for cognitive load management during HCW training and practice. Methods A comprehensive plan for participant inclusion and exclusion, data screening, and collection was established. This plan, outlined in Table 1 , used a population, concept and context (PCC), and population, intervention or exposure, comparison or control, and outcome (PICO) frameworks. Inclusion or exclusion of studies focused on relevant population (HCWs), intervention (cognitive load management strategies) and outcomes such as well-being (e.g., stress, burnout, anxiety), retention (e.g., job satisfaction and likelihood of continued working as a HCW), and performance (e.g., clinical skills, clinical reasoning, diagnostic accuracy, procedural competence, long-term application of learning, patient outcomes and error rates). Table 1 Eligibility frameworks (PCC and PICO) Framework Description PCC : P opulation C oncept C ontext Healthcare workers/trainees (students, residents, fellows, nurses, allied health) Cognitive load factors and management strategies strategies) Clinical pedagogy. We included empirical studies (experimental, quasi‑experimental, observational, qualitative) and, a priori for scoping breadth, select non‑empirical sources (protocols/consensus/tutorials) when they explicitly addressed cognitive load therapy‑aligned strategies or measurement. No date or language limits were applied. PICO : P opulation I (Intervention/Exposure) C (Comparison) O (Outcome) Clinical trainees (e.g., medical students, residents, nursing students, allied health students, intern, fellow, consultant) Cognitive load management strategies Traditional clinical training methods (i.e., clinical training without explicit cognitive load management strategies) Improved clinical skills, reduced errors, enhanced knowledge retention, increased confidence, HCW well-being, decreased stress/burnout, improved patient outcomes, long-term competency and HCW retention (with validated instruments) Abbreviations: PCC population, concept, and context. PICO population, intervention or exposure, comparison or control, and outcome. Research question: What cognitive load factors and management strategies are associated with improved well-being, retention, and performance among trainees in HCWs clinical training and practice? Review procedure: We conducted a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) 27 , followed by narrative synthesis. A narrative synthesis was selected to provide a broad overview and explore the conceptual foundations and scope of cognitive load management in clinical training, rather than to produce a narrowly focused, quantifiable estimates. Rayyan-a web and mobile app for systematic reviews 28 was used for this review. A medical librarian refined the strategy and database selection (Embase, MEDLINE, Web of Science, and Cumulative Index to Nursing and Allied Health, CINAHL). Full strategies for each database are in Additional file 1, including Google Scholar and hand-searches. Two authors independently performed screening and full-text review of the databases, with conflicts resolved by discussion, based on: a) Records published up until 27 February 2026; b) population: clinical trainees or HCWs; c) focus: cognitive load factors or management strategies; d) outcomes: well-being, retention, and/or performance (skills, reasoning, errors, patient outcomes). Three co-authors charted data, corresponding to the columns in Additional file 2, and conflicts were resolved through discussions. Critical appraisal: No formal risk‑of‑bias appraisal (and meta-analysis) was performed, consistent with scoping review objectives. Synthesis: We grouped findings by cognitive load therapy construct (intrinsic/extraneous/germane) and outcome domain, using inductive thematic mapping. Registration: The scoping review was made pre-registered through the Open Science Framework (https://doi.org/10.17605/OSF.IO/VW9FN) on 26 November 2023, and last update on 27 February 2026. No deviations from the protocol occurred beyond updating Google Scholar and a hand‑search on 27 February 2026. Search strings, database additions and dates are in Additional file 1. Results The search strategy resulted in 8,395 records. Systematic searches were executed in Embase (n = 110), MEDLINE (n = 124), Web of Science (n = 188), and CINAHL (n = 66), on 18–19 February 2025. These were supplemented by Google Scholar and hand searches (n = 7,907) were completed on 27 February 2026 (Fig. 2 ). Google Scholar and hand-searches was limited to the first 500 records after deduplication. After deduplication, 813 records underwent title and abstract screening, and 306 records underwent full-text review. A total of 125 articles were included (Additional file 2; references 29 – 56 , 16,57–87 , 88−110 , 3,13,14,25,111–129 , 18,26,130–141 , 142−146 ). We found studies were predominantly from the USA (45), followed by Canada (18), Australia (17), the UK (10), Germany (9), the Netherlands (7), and Denmark (4) by corresponding author institution. Other countries are represented to a lesser extent (see Additional file 2). In the subsequent sections, we investigate cognitive overload factors (Fig. 3 ) and strategies for managing cognitive overload, including chunk-based practice with feedback, metacognition, situation awareness, reflection practices, digital training: simulation and artificial intelligence (AI)-based technologies (Table 2 , Fig. 4 – 5 ). Table 2 Cognitive load management strategies in clinical training Managing intrinsic load Decreasing extraneous load Optimizing germane load Gradually proceed from low- to high- complexity task Provide learners with worked solutions Introduce variability across tasks; prompt self‑explanations Proceed from low- to high- fidelity environments Part-task preparation Alternate tasks on varying patient presentations Chunk or part-task training Goal-free tasks completion Encourage trainees to self-explain Reduce task element interactivity by task repetition Avoid providing redundant and split information Create links between prior learning with new knowledge Adapted based upon work by Atkinson, Howie, van Merrienboer, Sweller and others 16 – 19 , 147 Chunk-based practice and training duration One important strategy for managing intrinsic load is the use of deliberate practice or repetition with focused feedback 65 , 103 , 114 , 117 , which may impact performance 90 . Deliberate practice involves breaking down complex tasks into smaller, manageable chunks and practicing each chunk exclusively (therefore, managing the intrinsic load) before integrating them into the whole task 16 – 19 , 147 . For instance, complex medical or surgical procedures can be segmented into discrete steps incorporating simplified task structures, allowing trainees to master each component before progressing to the full operation to enhance skill acquisition and retention 80 . Deliberate practice is a promising training method for procedures such as ultrasound-guided vascular access and thoracentesis 38 , 39 , 63 . This approach helps manage cognitive load by allowing trainees to focus on one aspect of the procedure at a time in a flexible manner, rather than trying to process all the information at once. Although exact amount of training needed to hone core skills is still unknown, multiple studies 50 , 99 , 110 , 132 , 148 have indicated that concise, focused training sessions ranging from several hours to multiple sessions can efficiently train HCWs in hands-on tasks. The gradual incorporation of terminally branching schemes 46 into training programmes (i.e., adopting a step-by-step learning approach) may be associated with enhanced diagnostic accuracy, minimised perceptual errors, and reduced cognitive load; thereby, improving clinical decision-making. Furthermore, Yeo and Romero 140 recommend three processes for acquiring expert sonography skills: extensive training, deliberate practice, and performance evaluation/feedback. Metacognition Metacognition is described as “thinking about thinking” and is the ability to be aware of one's own thinking processes and to use this awareness to improve learning 149 – 151 . It involves monitoring one's understanding of own thought processes, identifying one's strengths and weaknesses, and selecting appropriate strategies for learning 149 – 151 . Metacognition is an important skill for both trainees and experts, and it can be improved with practice. Metacognition is not directly tied to either self-reflection or a supervised process, although it can be related to both. Self-reflection constitutes a specific form of metacognition, where individuals actively contemplate their own thoughts, feelings, and behaviours. Supervised processes, on the other hand, can involve external guidance in shaping one's thinking. For example, a senior physician might ask questions to help trainee understand their own thought process. While metacognition can be effectively utilised within supervised settings, it is not inherently dependent on them. The cognitive errors can be caused by several factors 33 , 56 , including personal, workplace, and patient-related issues, which may impact trainees’ confidence and well-being. The metacognitive processes could be tailored against factors associated with cognitive errors. Employing metacognitive strategies, such as structured learning scaffolds and reflective practice on personal experiences, could potentially promote anxiety management skills within nursing practitioners 89 . Situation awareness Chen et al. suggested that situational awareness is learnable and measurable task. They developed a surgical adaptation of a validated situational awareness assessment technique 51 . Situational awareness was divided into three domains: understanding (i.e., familiarity of operating room and role of different team members in the operating room), attentional demand (stability, complexity, and demand of operating room) and attentional supply (alertness, concentration, attention, and spare mental capacity). Chen et al. found students/clerks have lower baseline situational awareness scores and higher perceived complexity of a surgical procedure. Trainees’ situational awareness was significantly boosted, particularly in the understanding domain, after the clerkship period (up to 18 weeks). However, attentional demand and supply might require longer periods of clerkship training 51 . Reflection practices and leveraging prior knowledge Morrill et al. studied prevalence and types of operative reflection practices among surgical residents in a US hospital setting 106 . Three primary methods of post-operative reflection were mental, written, and oral. Among these, mental reflection was the most common method including step-by-step mental rehearsal, internal narration, and question series. For example, pre-operative 3D segmentation was reported to enhance the accuracy of surgical planning and reduced the time required while optimising the cognitive load 138 , 139 . The combination of demographic and cognitive load variables produced more sensitive metrics to predict ultrasound simulator performance in a proof-of-principle study from the University of Toronto 30 . In terms of acquiring ultrasound images, seasoned users fared better than novices. The two groups' interpretations of the ultrasound images were similar. The degree of training, previous ultrasound training, and cognitive load all predicted the performance of ultrasound image capture 30 . A nationwide Australian cross-sectional survey revealed five key factors that influence pedagogical approaches to teaching ultrasound scanning skills, including limited protected teaching time, perceived skill complexity, learner skill level and credentials, and avoiding overwhelming the learner, and patient well-being and willingness to be scanned 109 . Digital training: simulation During a simulation-based resuscitation programme 41 , 78 , trainees who used preparatory online courses saw changes in cognitive load that were consistent with cognitive optimisation, which may lead to subsequent superior performance. Preclinical simulation may be associated with student performance, reduced clinical staff burden, and improved patient safety during early ultrasound training 49 . Similarly, one month after ultrasound simulation training 49 , students showed a notable transfer of acquired ultrasound skills, attaining an average score of at least 3 out of 5, indicating advanced beginner proficiency in both complex psychomotor and patient-sonographer communication skills. Access to robotic platforms during training varies across systems. Robotic surgery began in the US 152 in the year 2000 and in Australia in 2003 153 . Surgical trainees in Australia 153 and the UK 154 spend seven years training; exposure can be inconsistent in some public settings. Some studies reported that virtual reality simulation training for mastoidectomy may reduce cognitive load compared to traditional cadaveric dissection for novices, underscoring the need to align modality, task complexity, and learner level 34 – 36 . AI-based technologies Bostrom 155 et al. identified several shortcomings in current training methods (a consensus from the US leaders in Orthopaedic training programs). Firstly, training programs are not fully exploiting available technologies and innovations to enhance the learning experience. Secondly, trainees are often using electronic resources inappropriately, either without guidance or in the absence of a structured electronic training program. Examples of potentially inappropriate electronic resources for medical and surgical trainees include unreliable websites, educational websites authored by medical practitioners but lacking peer review, and resources of low quality. Thirdly, training programs often fail to provide individualized instruction to cater to the different learning paces and styles of trainees. Finally, formal electronic learning programs can make better use of trainees' time and resources compared to informal learning methods. In essence, Bostrom 155 et al. advocated for a more modern and effective approach to training that incorporates technological advancements, structured electronic learning programs, and individualized instruction to optimize the learning experience for all trainees. Interestingly, Turkish medical students perceived that while AI-based technologies could assist physicians’ access to information (86%) and reduce errors (71%), many of them (45%) were worried about the possible of unemployment due to AI-based tools 53 . An AI-derived knowledge recommender for radiology education has the capacity to decrease reporting times, enhance diagnostic accuracy, and alleviate overall workload and mental strain for radiology trainees 97 . Ambient AI platforms have also been reported to reduce documentation burdens and self-reported cognitive load among HCWs; trainee-specific effects require further study 144 , 145 . Workplace environment and demonstrable outcomes The evidence suggests that feeling valued by one's organisation may be associated with several positive outcomes 13 , 14 , 26 , including reduced burnout, improved job satisfaction, and increased patient satisfaction. A survey from the US found that feeling valued by one's organisation was associated with lower odds of reducing hours or intending to leave 14 . Discussion By synthesising cognitive load factors and management strategies, this review provides a framework for enhancing trainee well-being and professional performance. Our findings indicate that elevated cognitive load is implicated in diminished HCW retention and well-being, correlating with heightened stress and burnout, thereby increasing turnover and reducing job satisfaction 1–3,5−8 . The relationship between cognitive load and performance is more complex with a marginal positive association, suggesting optimal cognitive load may enhance performance 30 , 64 , 78 ; however, it is plausible that cognitive overload can impair performance. Trainees should be informed of the risks due to high allostatic load, taking precautions, and being prepared for the unexpected 3 , 20 . Existing evidence shows that some groups may face additional competing demands that can contribute to cognitive overload. For example, poorly performing students, who may need personalised and research-led interventions after clinical examination failure 156 , or female trainees and physicians who may experience additional competing demands, such as breastfeeding at work and pregnancy related complications 157 – 159 . Cognitive load theory principles provide a coherent lens to interpret mixed performance finding across modalities and learner levels 17 – 19 , 147 . These strategies can help trainees cope with the sudden changes in demands or cognitive load and enhance their learning and performance. Deliberate practice is characterised by repetition, feedback, and progressive refinement 65 , 103 , 114 and enables learners to systematically and efficiently develop both technical and cognitive competencies 59 , 103 . When combined with the streamlining of procedures from complex to simple, deliberate practice enhances skill retention, may be associated with reduced cognitive overload, and improvement of overall performance in clinical tasks 59 , 65 , 103 , 140 . Our results highlight that deliberate practice, which is goal-oriented, sequential, and tailored to the learner’s level, can help trainees adjust their approach and improve their performance 16 – 19 , 50 , 147 . Knowledge must be set up in your mind in a flexible, accessible manner that is predicated on a sizable and connected body of clinical cases so that HCWs can automatically recognise patterns based upon past experiences and commence strategies to systematically and logically analyse data to solve clinical cases. For example, rash, tiredness, and restless legs may seem unrelated at first until a HCW consider gastrointestinal causes, which may imply coeliac disease with secondary anaemia. Research indicates that learners benefit from a stepwise, scaffolded approach, transitioning from low-fidelity to high-fidelity simulations and gradually increasing task complexity as competency improves 80 , 89 , 103 . Streamlining procedures for reduction of extraneous cognitive load include use of goal-free tasks, worked examples, and completion problems 18 . Research has demonstrated that distributed practice results in superior retention of psychomotor skills compared to massed practice (all practices performed in 1 day), as regular reinforcement facilitates the gradual consolidation of both motor and cognitive components 35 , 36 . Time-distributed practice consistently outperforms massed practice, especially for complex psychomotor skills, as it enables learners to refine their performance over multiple sessions rather than relying on an intensive, single-session approach 35 , 36 . Effective medical education frequently relies on structured instructional frameworks incorporating mnemonics, case-based learning, and modular, interactive educational designs to enhance skill development and knowledge retention. Interactive educational designs can integrate simulation-based learning, spaced learning, and test-enhanced learning methodologies to optimise long-term retention and cognitive efficiency 31 , 33 , 86 , 141 . Early integration of clinical reasoning instruction in medical school can improve clinical performance by familiarising students with diagnostic frameworks, structured decision-making, and the real-world application of knowledge 33 . This approach not only strengthens clinical interpretation skills but also enhances confidence and preparedness in patient care settings. However, increasing instructional authenticity may not consistently improve clinical reasoning performance across all outcome measures and subject areas 93 . Case-based learning (CBL) is a highly effective instructional method that improves diagnostic accuracy and medical interpretation skills 56 , 98 , 126 . Research indicates that the use of CBL in combination with expert-generated schemas significantly enhances clinical interpretation, particularly in complex areas such as ECG rhythm analysis 45 . By engaging with realistic patient scenarios, students can develop a systematic approach to problem-solving, reinforcing both theoretical knowledge and practical application. Situational awareness is important for managing cognitive load via optimising germane load and honing procedural skills 51 , 106 , 160 . Team-based learning, by harnessing situational awareness, can support cognitive load by allowing trainees to share the load of learning new information and skills. Medical and surgical trainees are usually intrinsically motivated and can monitor their cognitive processes in action (e.g., self-reflect for biases or errors in their reasoning to amend them) by optimizing their germane load 16 – 19 , 147 . By reviewing the recorded training sessions (self-assessment), HCWs can address procedures/techniques where they may have struggled or made errors to become more proficient in the respective techniques. During reflective training, residents can review their interactions with other team members and identify areas where they can improve their communication and teamwork skills. Prior self-directed and experiential learning (such as surgical observership or volunteering) makes the trainee comfortable with the operating room environment. Prior knowledge is important in enhancing the efficiency of learning, alleviating cognitive load, and optimising performance across diverse domains within medical training 29 , 49 , 51 , 54 , 65 , 92 , 117 , 126 . Individuals with extensive expertise and prior experience exhibit superior performance in skill-based tasks, such as the acquisition of ultrasound images and the management of airways 29 , 30 , 32 . Medical training should be designed to effectively manage cognitive demand in accordance with learners' levels of expertise 50 , 108 . Novices and experts process information in distinct ways, necessitating tailored instructional strategies to optimise learning, enhance performance, and mitigate cognitive overload. While novices benefit from structured guidance and immediate feedback within controlled environments, experts derive greater value from challenging, error-based learning and opportunities for delayed reflection to refine their decision-making capabilities 49 , 65 , 92 . Situational learning is a subjective measure, and triangulation with objective techniques may strengthen inferences. Simulation training provides a safe environment for trainees to practice history taking, physical examination, surgical procedures and develop the processes and strategies of clinical reasoning without the risk of harming patients. By allowing trainees to practice in a controlled setting, simulation training may reduce the cognitive load associated with patient encounters or when performing surgery for the first few times, which can eventually help them become more confident and competent in their abilities. Despite this, the simulated learning environment presents a complex and cognitively demanding scenario, necessitating careful consideration of the learner's prior experience in managing cognitive load 31 , 34 – 36 , 38 , 39 , 41 , 44 , 49 , 54 , 58 , 61 , 62 , 70 . Simulation and prior clinical observerships may further enhance trainees’ performance through harnessing situational awareness, along with deliberate practice and structured micro- or bio-breaks 29 , 51 , 119 . The use of simulated patient encounters (or cases) and simulation-based training for procedural skills (e.g., ultrasound 31 ) during the pre-clinical years or in early specialist training period 33 may accelerate early skill acquisition before clinical application; effects on training duration are unclear 153 . Simulation can help to manage extraneous cognitive load 16 , 25 . Pedagogical strategies incorporating time-distributed practice and virtual reality simulation demonstrate potentially fostering the sustained retention and reinforcement of complex psychomotor skills, notably those essential for the performance of mastoidectomy 35 , 36 . Although more cognitively demanding and time-intensive, students reported gaining a deeper understanding of complex tasks within simulated clinical immersion compared to simpler tasks. These experiences pose additional challenges to clinical reasoning, ultimately offering a more enriching and valuable learning experience from the students' perspective 129 . Moreover, simulation-based deliberate practice in procedures such as airway management and central venous catheterisation may be associated with lower error rates in some procedural settings 32 , 65 . By integrating simulator-based training with real-time feedback, trainees can likely refine their skills in low-risk environments before applying them in clinical settings 80 , 140 . Digital training (e.g., online medical courses and surgical simulations) may enhance trainees’ situational awareness and optimise germane load 16 , 25 , 51 , 119 , 133 , 141 . One important benefit of digital learning is the opportunity to provide students with immediate feedback and/or adapt the learning content to the learning ability of students. For example, some students might get exhausted faster than the others and the same student might also have different learning ability over days. As such customising these learning processes to make it adaptive and personally relevant would make it less cognitively intense. Cognitive load may contribute to simulation-based learning failures 70 and further research is needed to determine how this understanding should inform simulation design. Integrating cognitive load measures into ongoing simulation research could quantify the theory's relevance to our field and provide an additional framework for interpreting emerging findings 70 . AI-based platforms may support physicians and trainees who are struggling with time management due to documentation requirements 53 , 144 , 145 via summarising individual electronic health records. However, large language models encoding clinical knowledge could also trigger unwarranted stress of misdiagnoses or unnecessary interventions 161 . External factors such as information retrieval, interruptions, reporting software, and time pressure may contribute to extraneous load especially in cardiology trainees 47 . Shared workspace, access to attendings, and availability of final reports foster germane load 47 . Creating a positive learning environment may support HCW training, as it helps to enhance learning outcomes and improves the overall quality 14 , 91 . Physicians and medical students are more likely to make mistakes if they are hungry, angry, late, or tired 40 . Another key strategy for mitigating this is implementing safety breaks, which address both physical, cognitive and emotional depletion. Simple strategies such as hydration reminders, structured rest periods, and workplace policies that prioritise staff well-being can likely improve performance and job satisfaction 29 , 40 . HCWs who experience emotional exhaustion, detachment from work (depersonalisation), and a diminished sense of personal accomplishment, may have higher attrition rates and a potentially decline in the quality of patient care 13 , 26 , 55 , 81 . Workshops and interventions focusing on workload management, mindfulness, and structured breaks are important in creating sustainable learning environments and enhancing resilience among HCWs and students 55 . Addressing these challenges through targeted strategies can potentially mitigate burnout, reduce stress-related errors, and improve overall job satisfaction 104 . It is important to measure baseline values and monitor the state of HCW well-being to identify at risk HCWs, and likely support them with a certified physician assistant, trained peer team and mental health experts 55 . There are several ways to measure HCW well-being. Some common measures include self-reported surveys and biomarkers to assess the physical and psychological effects of stress. These surveys 162 ask HCWs about their levels of stress, anxiety, depression, and other mental health problems. Likewise, it could be useful to measure cognitive load during procedural skill training 163 to better manage cognitive load and stresses in training period. It's important to prioritise evidence-based surveys and technologies tailored to the local training context. The HCW mental health is complex and requires a comprehensive approach involving stakeholders at all levels of the healthcare system. At the individual level, junior HCWs can learn adaptive coping skills through cognitive behavioural therapy and mindfulness techniques 60 , 112 , and they should be provided with specialised mental health services 81 . Senior HCWs can reinforce adaptive coping skills in their junior colleagues 81 . Retiring HCWs should be given appropriate preparation for retirement and be offered ongoing roles in teaching and training 81 . At the institutional level, medical schools can teach students effective coping skills and normalize help-seeking behaviour 81 . Hospitals and health systems can support administrative burdens, ensure workplace autonomy, and provide job security for HCWs 81 , by focusing on interventions such as leadership development, strategic recruitment, and adequate staffing and resources 104 . Professional colleges and external regulators can consider implementing individually focused interventions such as offer personnel selection for supervisory roles, mandatory reporting rules, flexible leave arrangements, and work hours and shifts, where feasible 81 . The consensus statement 91 from New Zealand and Australia recommends that medical schools promote student well-being through peer support, stress management, mental health promotion, physical health promotion, and a safe and health-promoting learning environment. In addition, implementing policies that aim to increase financial rewards, offer opportunities for professional growth, promote flexibility, and build employees’ strong ties to the community may help to enhance HCW retention 95 . Institutional interventions can complement individual coping strategies and help HCWs manage stress more effectively 104 . HCWs may benefit from relaxation techniques (such as mindfulness-based practices, yoga, meditation, and acupuncture) and having a positive mindset 55 , 60 , 73 , 112 . Burnout-awareness workshops equip HCWs with the tools to recognise early warning signs and adopt preventive measures, such as maintaining a work-life balance, utilising peer support systems, and adjusting workloads 26 , 55 , 104 . High levels of stress in students, arising from uncertainties in medical management, can negatively impact their cognitive processes 76 . However, familiarity with the practice setting, anticipation of challenges, debriefing sessions, the provision of a safe learning environment, and future training opportunities can help students manage stress effectively, thereby transforming simulations into positive learning experiences 51 , 58 , 80 , 124 , 129 , 131 . Consequently, by integrating these approaches into professional development programmes, healthcare institutions can prevent burnout and improve workforce retention. Mental rehearsal practice may be associated with improved procedural performance 88 , likely reducing stress by enabling individuals to visualise complex procedures or decision-making scenarios. Mindfulness-based interventions have demonstrated efficacy in reducing burnout by enhancing attentional control, emotional regulation, and stress resilience 26 , 55 , 60 , 73 , 112 . Research indicates that mindfulness training diminishes medical errors, improves decision-making under pressure, and promotes overall well-being 92 , 112 . These interventions can be seamlessly incorporated into daily routines or delivered through structured formats such as workshops, guided reflection sessions, and stress management programmes, thereby fostering long-term resilience among healthcare professionals. When trainees and medics use their skills and knowledge to make a positive difference in the lives of others, they may find happiness and fulfilling life 55 , 73 . This review is not without its limitations. The task of interweaving cognitive load, well-being, and performance into a cohesive review proved challenging. We sought to reduce retrieval bias by including four databases and Google Scholar/hand‑searches; however, publication bias cannot be excluded. There is a possibility that we may have unintentionally overlooked relevant studies that did not employ specific keywords associated with cognitive load and well-being. This oversight could result in a less comprehensive grasp of the existing evidence, potentially skewing our understanding of the relationship between the factors identified and the success of trainees. Conclusion The cognitive load theory inspired strategies may support HCWs to enhance their well-being, learning and performance, by using deliberate practice, instructional frameworks (structured learning, mnemonics, and case-based learning), situation awareness, reflection practices and digital training. Resource levels, staffing models, case-mix, and sociocultural factors likely moderate both cognitive load and intervention effectiveness, although standardised measurement and trainee‑centred outcomes (well‑being/retention) remain under‑studied. To ensure a more global perspective and help reduce avoidable sources of cognitive overload and support well-being, further research on cognitive load factors and management strategies is necessary, particularly from underrepresented regions such as Asia, Africa, South America, parts of Eastern Europe, the Middle East, and Oceania beyond Australia. Declarations Author contributions : AB and JS developed the concept of the paper. AB and LL performed literature search using four databases, Google Scholar, hand searching; and screening and full-text review using Rayyan Review Management Platform, shown in the additional file 1. AB, RL and SB extracted the study data shown in the additional file 2. AB wrote the original manuscript and created the figures. AB, RL, LL, SB and NQL provided critical input, revisions, and contributed to the interpretation of the reviewed findings. AB amended the original draft. All authors reviewed and contributed to the work presented. Acknowledgements: We thank and appreciate Malik Beglerovic, librarian at the University of Bergen, Norway, for helping us in refining and updating the search strategy. AB extends thanks to colleagues at the University of Melbourne’s Teaching Certificate program, University of Bergen, and worldwide for their support 164,165 . Funding: AB received funding from the University of Bergen, The National Graduate School in Infection Biology and Antimicrobials (or IBA) and Pasteur legatet & Thjøtta’s legat, University of Oslo, Norway [101563]. Potential conflicts of interest: Authors report no potential conflict of interest. Ethics approval : Ethics approval was not required for the preparation of this article. Data availability : Data sharing is not applicable to this article as no new datasets were generated or analysed during the current study. References Sletta C, Tyssen R, Lovseth LT (2019) Change in subjective well-being over 20 years at two Norwegian medical schools and factors linked to well-being today: a survey. BMC Med Educ 19:45 Vogel L (2019) US doctors call for tracking of suicides among medical trainees. CMAJ 191:E26 Stacey A et al (2022) Warning: medical education is hazardous to your mental health. Medical students should make an informed decision to begin and continue training. Can Med Educ J 13:39–44 Almutairi H et al (2022) Prevalence of burnout in medical students: A systematic review and meta-analysis. Int J Soc Psychiatry 68:1157–1170 Chang E, Eddins-Folensbee F, Coverdale J (2012) Survey of the prevalence of burnout, stress, depression, and the use of supports by medical students at one school. Acad Psychiatry 36:177–182 Altannir Y et al (2019) Assessment of burnout in medical undergraduate students in Riyadh, Saudi Arabia. BMC Med Educ 19:34 Barbosa ML et al (2018) Burnout Prevalence and Associated Factors Among Brazilian Medical Students. Clin Pract Epidemiol Ment Health 14:188–195 Muaddi MA et al (2023) Assessment of Medical Students Burnout during COVID-19 Pandemic. Int J Environ Res Public Health 20 de la Vega Sanchez D, Irigoyen-Otinano M, Carballo JJ, Guija JA, Giner L (2023) Suicidal thoughts and burnout among physicians during the first wave of the COVID-19 pandemic in Spain. Psychiatry Res 321:115057 Khajuria A et al (2021) Workplace factors associated with mental health of healthcare workers during the COVID-19 pandemic: an international cross-sectional study. BMC Health Serv Res 21:262 Trieu MC et al (2021) SARS-CoV-2-Specific Neutralizing Antibody Responses in Norwegian Health Care Workers After the First Wave of COVID-19 Pandemic: A Prospective Cohort Study. J Infect Dis 223:589–599 Bansal A et al (2023) Risk assessment and antibody responses to SARS-CoV-2 in healthcare workers. Front Public Health 11 Shanafelt TD et al (2022) Changes in Burnout and Satisfaction With Work-Life Integration in Physicians During the First 2 Years of the COVID-19 Pandemic. Mayo Clin Proc 97:2248–2258 Sinsky CA, Brown RL, Stillman MJ, Linzer M (2021) COVID-Related Stress and Work Intentions in a Sample of US Health Care Workers. Mayo Clin Proc Innov Qual Outcomes 5:1165–1173 Schnotz W, Kurschner C (2007) A reconsideration of cognitive load theory. Educ Psychol Rev 19:469–508 Howie EE et al (2023) Cognitive Load Management: An Invaluable Tool for Safe and Effective Surgical Training. J Surg Educ 80:311–322 Sweller J, van Merriënboer JJG, Paas F (2019) Cognitive Architecture and Instructional Design: 20 Years Later. Educ Psychol Rev 31:261–292 van Merrienboer JJ, Sweller J (2010) Cognitive load theory in health professional education: design principles and strategies. Med Educ 44:85–93 Sweller J, van Merrienboer JJG, Paas FG (1998) W.C. Cognitive Architecture and Instructional Design. Educ Psychol Rev 10:251–296 McEwen BS (2007) Physiology and neurobiology of stress and adaptation: central role of the brain. Physiol Rev 87:873–904 Rahić O, Tucak A, Sirbubalo M, Hindija L, Hadžiabdić J (2021) Antihypertensives’ Rock around the Clock. J 4:62–81 Tucak-Smajić A (2023) Stress Response and Development of Allostatic Load Soni N, Arntfield R, Kory P (2014) Point of Care Ultrasound S aunders Gerjets P, Scheiter K, Cierniak G (2009) The Scientific Value of Cognitive Load Theory: A Research Agenda Based on the Structuralist View of Theories. Educ Psychol Rev 21:43–54 Tokuno J, Carver TE, Fried GM (2023) Measurement and Management of Cognitive Load in Surgical Education: A Narrative Review. J Surg Educ 80:208–215 West CP, Dyrbye LN, Shanafelt TD (2018) Physician burnout: contributors, consequences and solutions. J Intern Med 283:516–529 Tricco AC et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169:467–473 Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan—a web and mobile app for systematic reviews. Syst Reviews 5:210 Aalioui L, Gouzi F, Tricot A (2022) Reducing cognitive load during video lectures in physiology with eye movement modeling and pauses: a randomized controlled study Aldekhyl S, Cavalcanti RB, Naismith LM (2018) Cognitive load predicts point-of-care ultrasound simulator performance. Perspect Med Educ 7:23–32 Alexander LF et al (2023) Ultrasound Simulation Training for Radiology Residents-Curriculum Design and Implementation. J Ultrasound Med 42:777–790 Ambardekar AP, Rosero EB, Bhoja, Ravi, Green J, Rebal BA, Minhajuddin, Abu (2019) … A Randomized Controlled Trial Comparing Learners' Decision-making, Anxiety, and Task Load During a Simulated Airway Crisis Using Two Difficult Airway Aids Amey L, Donald KJ, Teodorczuk A (2017) Teaching clinical reasoning to medical students. Br J Hosp Med (Lond) 78:399–401 Andersen SA, Mikkelsen PT, Konge L, Caye-Thomasen P, Sorensen MS (2016) Cognitive Load in Mastoidectomy Skills Training: Virtual Reality Simulation and Traditional Dissection Compared. J Surg Educ 73:45–50 Andersen SAW, Konge, Lars C-T (2016) Per, Sorensen, Mads Solvsten. Retent Mastoidectomy Skills After Virtual Real Simul Train Andersen SAW, Mikkelsen P, Trier (2016) Konge, Lars, Cayé-Thomasen, Per, Sørensen. … Cogn load Distrib massed Pract virtual Real mastoidectomy Simul Balslev T, Muijtjens A, de Grave W, Awneh H, van Merrienboer J (2021) How isolation key Inform allowing clarifying questions may improve Inform Qual Diagn accuracy case handover paediatrics Barsuk JH, Ahya SN, Cohen ER, McGaghie WC, Wayne DB (2009) Mastery learning of temporary hemodialysis catheter insertion by nephrology fellows using simulation technology and deliberate practice. Am J Kidney Dis 54:70–76 Barsuk JH, McGaghie WC, Cohen ER, O'Leary KJ, Wayne DB (2009) Simulation-based mastery learning reduces complications during central venous catheter insertion in a medical intensive care unit. Crit Care Med 37:2697–2701 Baverstock A, Finlay F (2019) Take a break: HALT-are you Hungry, Angry, Late or Tired? Arch Dis Child Educ Pract Ed 104:200 Behrens C, Dolmans, Diana HJM, Leppink, Jimmie, Gormley GJ (2018) Driessen, Erik W. Ward round simulation in final year medical students: Does it promote students learning? Bellstedt M (2024) Holtrup, Adrian, Otto, Nils, Berndt, Markus, Scherff, Aline Doreen, Papan, C… Gaze cueing improves pattern recognition of histology learners Ben Awadh A, Clark J, Clowry G, Keenan ID (2022) Multimodal Three-Dimensional Visualization Enhances Novice Learner Interpretation of Basic Cross-Sectional Anatomy Birt J, Moore E, Cowling M (2017) Improving paramedic distance education through mobile mixed reality simulation Blissett S, Cavalcanti R, Sibbald M (2015) ECG rhythm analysis with expert and learner-generated schemas in novice learners Blissett S, Morrison D, McCarty D, Sibbald M (2017) Should learners reason one step at a time? A randomised trial of two diagnostic scheme designs Blissett S, Rodriguez S, Qasim A (2021) O'Sullivan, P. Learning Echocardiography in the Workplace: A Cognitive Load Perspective. Acad Med 96:441–448 Bostrom M, Lewis KO, Berger G, Orthopaedic Elect Learning Grad Me (2021). The Role of Electronic Learning in Orthopaedic Graduate Medical Training: A Consensus From Leaders in Orthopaedic Training Programs Bowman A, Reid D, Bobby Harreveld R, Lawson C (2021) Evaluation of students' clinical performance post-simulation training. Radiography (Lond) 27:404–413 Brzezinski M, Kukreja, Jasleen M (2019) John D. Time-efficient, goal-directed, and evidence-based teaching in the ICU Chen JJ, Gompers A, Evenson A, James BC, Royce C (2023) Surgical Adaptation of the Situation Awareness Rating Technique (S-SART): Assessing Situational Awareness Among Medical Students. J Surg Educ 80:216–227 Choi-Lundberg DL, Cuellar WA, Williams, Anne-Marie M (2016) Online dissection audio-visual resources for human anatomy: Undergraduate medical students' usage and learning outcomes Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A (2022) Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ 22:772 Clarke K, Alim A-MA, Nimer L, Hatfield A, Sabharwal E, Syed S (2024) S. Using Mixed Reality Simulation to Improve Junior Medical Trainees' Preparedness to Manage High-Acuity Trauma Cohen C, Pignata S, Bezak E, Tie M, Childs J (2023) Workplace interventions to improve well-being and reduce burnout for nurses, physicians and allied healthcare professionals: a systematic review. BMJ Open 13:e071203 General Practice Supervisors Australia (GPSA). Teaching clinical reasoning (Teaching guide) (2021) Danboise B, Italiya BS, Richman S (2022) P. 254 Does Video Pre-Briefing Reduce Cognitive Load During a Simulated ACLS Scenario? Dankbaar, M., Alsma, Jelmer, Jansen, Els, Merrienboer, Jeroen, Saase, Jan, Schuit,Stephanie, D… An experimental study on the effects of a simulation game on students' clinical cognitive skills and motivation , (2016) Donato AA, Kaliyadan AG, Wasser T (2014) Self-Directed Study Using MP3 Players to Improve Auscultation Proficiency of Physicians: A Randomized, Controlled Trial Dubert CJ (2013) A mindfulness model of emotion regulation in nursing students: Working memory capacity as a regulatory mechanism Dubovi I (2018) Designing for online computer-based clinical simulations: Evaluation of instructional approaches Dubovi I, Lee VR (2019) Instructional support for learning with agent-based simulations: A tale of vicarious and guided exploration learning approaches Duncan DR, Morgenthaler TI, Ryu JH, Daniels CE (2009) Reducing iatrogenic risk in thoracentesis: establishing best practice via experiential training in a zero-risk environment. Chest 135:1315–1320 Eickelmann AK, Huwendiek WNJ (2021) S. Teaching the technical performance of bronchoscopy to residents in a step-wise simulated approach: factors supporting learning and impacts on clinical work - a qualitative analysis Fernandes R, Malloy-Diniz LF, de Vasconcellos MC, Camargos PAM, Ibiapina C (2021) Adding guidance to deliberate reflection improves medical student's diagnostic accuracy Flynn S.G., P.R.S., Jena A.B., Staffa S.J., Kim S.Y., Clarke J.D., Pham I.V., Lukovits K.E., H… Coaching inexperienced clinicians before a high stakes medical procedure: randomized clinical trial , (2024) Fontaine G, Cossette S, Dube GM-P, Veronique, Cote J (2020) Effectiveness of a Theory- and Web-Based Adaptive Implementation Intervention on Nurses' and Nursing Students' Intentions to Provide Brief Counseling: Protocol for a Randomized Controlled Trial Fraser, K., Huffman, James, Ma, Irene, Sobczak, Matthew, McIlwrick, Joanne, Wright,Bruce, M… The emotional and cognitive impact of unexpected simulated patient death: a randomized controlled trial , (2014) Fraser K, Teteris MI, Baxter E, Wright H, McLaughlin B (2012) K. Emotion, cognitive load and learning outcomes during simulation training Fraser KL, Ayres P, Sweller J (2015) Cognitive Load Theory for the Design of Medical Simulations. Simul Healthc 10:295–307 Friedman MA, Has TL, Hampton P (2017) B.S. The impact of a postpartum rounding video module on medical student learning during the obstetrics and gynecology core clerkship Gao Y, Ceken BD (2022) C., Kow A.W.C. Mixed reality enhanced laparoscopic living donor liver transplant Gerrard KM, Shepherd J, Sehdev R, Gangathimmaiah V, Nagle C (2023) Meeting the wellness needs of emergency department clinicians: A scoping review of interventions. Australas Emerg Care 26:169–178 Grangeia T, de Jorge B, Franci D, Santos TM, Setubal MSV, Schweller M, de Carvalho MA (2016) Cognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance Greville WJ, Dymond, Simon N (2016) Philip M. The student experience of applied equivalence-based instruction for neuroanatomy teaching Groot F, Jonker G, Rinia M, ten Cate O, Hoff RG (2020) Simulation at the Frontier of the Zone of Proximal Development: A Test in Acute Care for Inexperienced Learners Guluma KZ, Brandl K (2023) Virtual learning allows for adaptation of study strategies in a cohort of U.S. medical students Gutierrez G, Lunsky IO, Van Heer S, Szulewski A, Chaplin T (2023) Cognitive load theory in action: e-learning modules improve performance in simulation-based education. A pilot study. CJEM 25:893–901 Hackett M, Proctor M (2018) The effect of autostereoscopic holograms on anatomical knowledge: a randomised trial Haji, F.A., Cheung, Jeffrey J H, Woods, Nicole, Regehr, Glenn, de Ribaupierre, Sandrine,Dubr… Thrive or overload? The effect of task complexity on novices' simulation-based learning , (2016) Harvey SB et al (2021) Mental illness and suicide among physicians. Lancet 398:920–930 Hashemi SB, Tan F, K.and, Bain CA (2019) Addressing Cognitive Load in Training on Electronic Medical Record Systems ACIS 2019 Proceedings. 56 Heer, S.V., Cofie, Nicholas, Gutierrez, Gilmar, Upagupta, Chandak, Szulewski, Adam,Chaplin,… Shaken and stirred: emotional state, cognitive load, and performance of junior residents in simulated resuscitation , (2021) Hempel G, Struck HW, Piegeler MF, Rotzoll T (2019) D. Impact of quantitative feedback via high-fidelity airway management training on success rate in endotracheal intubation in undergraduate medical students-A prospective single-center study Hill PP, Anderson DDA, Talbert M, Maraj S (2024) C. Remembering to Resume: A Randomized Trial Comparing Combined Interruption Management Training and Simulation-Based Education to Simulation-Based Education Alone Hirsch AW, Nagler J (2020) Spaced Education Through e-Learning for Ongoing Professional Development Hou M, Liu LY, Luo Y, Xue W, Yuan H, Zhang H (2024) X. The impact of virtual reality applied in nurse education on students' clinical thinking: A systematic review Ignacio, J., Dolmans, Diana, Scherpbier, Albert, Rethans, Jan-Joost, Lopez, Violeta,Liaw, … Development, implementation, and evaluation of a mental rehearsal strategy to improve clinical performance and reduce stress: A mixed methods study , (2016) Iwama Y, Ikeda M (2024) Exploring a metacognitive approach for case analysis based learning of anxiety adjustment in nurses: a qualitative study Jannin L, Ganier F, De Vries P (2019) Atomized or Delayed Execution? An Alternative Paradigm for the Study of Procedural Learning Kemp S et al (2019) Medical student wellbeing - a consensus statement from Australia and New Zealand. BMC Med Educ 19:69 Klein M, Otto, Barbel, Fischer MR, Stark (2019) Robin. Fostering medical students' clinical reasoning by learning from errors in clinical case vignettes: effects and conditions of additional prompting procedures to foster self-explanations La Rochelle JS, Pangaro DSJ, Artino LN, van der Vleuten AR, Schuwirth CP (2011) L. Authenticity of instruction and student performance: a prospective randomised trial Lee J, Donkers J, Jarodzka H, Sellenraad G, Faber TJE, van Merriënboer JJG (2024) The Effects of Reflective Pauses on Performance in Simulation Training Lin TK, Werner K, Kak M, Herbst CH (2023) Health-care worker retention in post-conflict settings: a systematic literature review. Health Policy Plan 38:109–121 Liu, A., Duffy, Melissa, Tse, Sandy, Zucker, Marc, McMillan, Hugh, Weldon, Patrick,Quet, Julie… Concurrent versus terminal feedback: The effect of feedback delivery on lumbar puncture skills in simulation training , (2023) Lopez-Rippe J., R.M., Velez-Florez M.C., Amiruddin R., Lerebo W., Gokli A., Francavilla M., R… RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load , (2024) Maia D, Andrade R, Afonso J, Costa P, Valente C, Espregueira-Mendes J (2023) Academic Performance and Perceptions of Undergraduate Medical Students in Case-Based Learning Compared to Other Teaching Strategies: A Systematic Review with Meta-Analysis Manasia AR et al (2005) Feasibility and potential clinical utility of goal-directed transthoracic echocardiography performed by noncardiologist intensivists using a small hand-carried device (SonoHeart) in critically ill patients. J Cardiothorac Vasc Anesth 19:155–159 Manning KD, Spicer JO (2021) Golub, Lucas, Akbashev, Mikhail, Klein, Robin. The micro revolution: effect of Bite-Sized Teaching (BST) on learner engagement and learning in postgraduate medical education Marei HF, Donkers J, Al-Eraky MM, Van Merrienboer JJG (2019) Collaborative use virtual patients after lecture enhances Learn minimal Invest Cogn load Margallo, M.C.M., Mariano, Carmela Nicole P., Martinez, Kryssia Nadine Fauve L.,Maria… Characterizing effective teaching in nursing pharmacology YouTube videos: A mixed methods study , (2023) McConnaughey S, Freeman, Rosario, Kim S, Sheehan F (2018) Integrating Scaffolding and Deliberate Practice Into Focused Cardiac Ultrasound Training: A Simulator Curriculum McCormick E, Devine S, Crilly J, Brough P, Greenslade J (2023) Measuring occupational stress in emergency departments. Emerg Med Australas 35:234–241 Modi H, Singh H, Orihuela-Espina F, Athanasiou T, Fiorentino F, Yang GZ, Darzi A, Leff DR (2018) Temporal Stress Operating Room: Brain Engagem Promotes Coping Disengagement Prompts Choking Morrill C, Hardin J, Steiman J (2023) Perioperative Reflection: An Understudied Yet Common Practice Among Surgical Residents. J Surg Educ 80:428–433 Narayanan A, Kong NM, Pearson V, Mani L, Fisher K, Khashram JP (2024) M. Broad Responses and Attitudes to Having Music in Surgery (The BRAHMS Study): An Australia and Aotearoa New Zealand Perspective Natesan S, Bailitz J, King A, Krzyzaniak SM, Kennedy SK, Kim AJ, Byyny R, Gottlieb M (2020) Clinical Teaching: An Evidence-based Guide to Best Practices from the Council of Emergency Medicine Residency Directors Nicholls D, Sweet L, Hyett J, Muller A (2020) Push and pull factors impacting the pedagogical approaches used by sonographers to teach scanning skills. Australas J Ultrasound Med 23:220–226 Noble VE et al (2009) Evaluation of a thoracic ultrasound training module for the detection of pneumothorax and pulmonary edema by prehospital physician care providers. BMC Med Educ 9:3 Oade S, Storey SK, Timme J (2019) C. WORK TOGETHER, LEARN TOGETHER: THE BENEFITS OF INITIATING IN-SITU SIMULATION IN AN ACUTE MEDICINE DEPARTMENT Pandit A, de Gouveia M, Horsfall HL, Reka A, Marcus HJ (2022) Efficacy of a Mindfulness-Based Intervention in Ameliorating Inattentional Blindness Amongst Young Neurosurgeons: A Prospective, Controlled Pilot Study Passalacqua C, Malhan LM (2023) N. Reducing Cogn Overload Resusc Room Pusic MV et al (2012) Experience curves as an organizing framework for deliberate practice in emergency medicine learning. Acad Emerg Med 19:1476–1480 Rao Bhagavathula V, Bhagavathula, Viswakanth M, Chaudhuri RS, Joydeep Dutta (2022). The Integration of Prelaboratory Assignments within Neuroanatomy Augment Academic Performance, Increase Engagement, and Enhance Intrinsic Motivation in Students Richardson KM, Fouquet SD, Kerns, Ellen MC, Russell J (2019) Impact of Mobile Device-Based Clinical Decision Support Tool on Guideline Adherence and Mental Workload Ruiz JG, Levinson CD (2009) AJ. Computer animations in medical education: a critical literature review Sanderson, B.J., Field, Jeremy D, Kocaballi, Ahmet B, Estcourt, Lise J, Magrabi,Farah, Wood… Clinical decision support versus a paper-based protocol for massive transfusion: Impact on decision outcomes in a simulation study , (2023) Schick, K., Reiser, S, Janssen, L, Schacht, L, Pittroff, SID, Dorfler, E, Klein,E, Roenneberg, C,… Training in medical communication competence through video-based e-learning: How effective are video modeling and video reflection? (2024) Schlorhaufer C, Behrends M, Diekhaus G, Keberle M, Weidemann J (2012) Implementation of a web-based, interactive polytrauma tutorial in computed tomography for radiology residents: how we do it Schmitz F, Schnabel KP, Stricker D, Fischer MR, Guttormsen (2017) S. Learning communication from erroneous video-based examples: A double-blind randomised controlled trial Sewell JL, Cate BJ, O'Sullivan OT, Shah P, Boscardin BJ (2019) C. EXPERIENCED ENDOSCOPY TEACHERS USE TEACHING STRATEGIES THAT ADDRESS LEARNER CHALLENGES AND ALIGN WITH COGNITIVE LOAD THEORY Sewell JL, Boscardin YJQ, Cate C, O'Sullivan OT (2019) P. IMPACT OF SPECIFIC TEACHING STRATEGIES ON FELLOW COGNITIVE LOAD DURING COLONOSCOPY TRAINING Sim, J.J.M., Rusli, Khairul Dzakirin Bin, Seah, Betsy, Levett-Jones, Tracy, Lau,Ying, Li… Virtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis , (2022) Spiller K, Ossenberg C, Takashima M, Henderson A (2025) Comparable achievement of workplace performance of student and graduate nurses: A quantitative cohort evaluation Stark R, Kopp V, Fischer MR (2009) Facilitating Medical Students' Diagnostic Competence Via Situated, Case-Based Learning With Worked-Out Examples: The Influence of Errors and Feedback Styn A, Scheiter K, Fischer MR, Shiozawa T, Behrmann F, Steffan A, Kugelmann D, Berndt M (2023) Effects of tablet-based drawing and paper-based methods on medical students' learning of gross anatomy Taveira-Gomes T, Prado-Costa R, Severo M, Ferreira MA (2015) Characterization of medical students recall of factual knowledge using learning objects and repeated testing in a novel e-learning system Tremblay M-L, Leppink, Jimmie, Leclerc G, Rethans J-J, Dolmans, Diana HJM (2019) Simulation-based education for novices: complex learning tasks promote reflective practice Uraiby H, Gordon G-CC, Sereno M, Powell M, McCarthy B (2021) M. Fostering intrinsic motivation in remote undergraduate histopathology education Uslu-Sahan F, Ozdemir, Leyla K, Mert M, Yildirim, Sevda, Odabasi O (2025) Virtual reality and standardized patient simulation programs in chemotherapy education for breast cancer: A comparative analysis of nursing students' knowledge, cognitive load, satisfaction and confidence Vignon P et al (2007) Focused training for goal-oriented hand-held echocardiography performed by noncardiologist residents in the intensive care unit. Intensive Care Med 33:1795–1799 Vydro SA, Drapeau LC, Perez S, Romero A, Norris D, Harmon M, Sewell D (2023) J. Implementation of a 3D-Printed Color-Coded Tracheobronchial Tree for Bronchoscopy Training and Anatomy Learning in a Pulmonary. Crit Care Fellowsh Program Wahyuni S, Cahyati B, Limakrisna P (2020) N. The Influence of Self Efficacy on Cognitive Load In Online Learning In The Pandemic Covid-19 On Nursing Students Wang T, Zheng J, Tan CY, Lajoie SP (2023) Computer-based scaffoldings influence students' metacognitive monitoring and problem-solving efficiency in an intelligent tutoring system Werner A, Holderried F, Schaffeler N, Weyrich P, Riessen R, Zipfel S, Celebi N (2013) Communication training for advanced medical students improves information recall of medical laypersons in simulated informed consent talks - a randomized controlled trial Wolf J, Lohmeyer LD, Farshad Q, Furnstahl M, Meboldt P (2023) M. How different augmented reality visualizations for drilling affect trajectory deviation, visual attention, and user experience Yeo C, Ungi MA, Lasso T, Jalink A, Fichtinger D, Nanji G (2017) S. 3D segmentation as a surgical planning tool for residents in liver resection surgery Yeo, C.T., MacDonald, Andrew, Ungi, Tamas, Lasso, Andras, Jalink, Diederick, Zevin,Boris, F… Utility of 3D Reconstruction of 2D Liver Computed Tomography/Magnetic Resonance Images as a Surgical Planning Tool for Residents in Liver Resection Surgery , (2018) Yeo L, Romero R (2022) Optical ultrasound simulation-based training in obstetric sonography. J Matern Fetal Neonatal Med 35:2469–2484 Yiin S-J, Chern C-L (2023) The effects of an active learning mechanism on cognitive load and learning achievement: A new approach for pharmacology teaching to Taiwanese nursing students McNamara T et al (2025) Short and Sweet: Reducing Extrinsic Cognitive Load When Authoring Self-Study Materials for Medical Students. Med Sci Educ Olson AS et al (2025) Assessment of Emergency Medicine Residents' Situational Awareness and Perception of Patient Safety Culture in the Emergency Department. AEM Educ Train 9:e70060 Olson KD et al (2025) Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Netw Open 8:e2534976 Hudson TJ et al (2025) Impact of Ambient Artificial Intelligence Documentation on Cognitive Load. Mayo Clin Proc Digit Health 3:100193 Tsyrulnik A et al (2026) When SMART Isn't Always Best: An Investigation of Emergency Medicine Resident Goal Setting Practices. AEM Educ Train 10:e70125 Ashman G, Kalyuga S, Sweller J (2020) Problem-solving or Explicit Instruction: Which Should Go First When Element Interactivity Is High? Educ Psychol Rev 32:229–247 Blaivas M, Lambert MJ, Harwood RA, Wood JP, Konicki J (2000) Lower-extremity Doppler for deep venous thrombosis–can emergency physicians be accurate and fast? Acad Emerg Med 7:120–126 Fleur DS, Bredeweg B (2021) Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. NPJ Sci Learn 6:13van den Pintrich PR (2002) The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing. Theory Into Pract 41:219–225 Zimmerman BJ (1990) Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychol 25:3–17 Shah J, Vyas A, Vyas D (2014) The History of Robotics in Surgical Specialties. Am J Robot Surg 1:12–20 Costello Anthony (2023) Junior surgeons must be trained for robotics revolution. MJA Insights Fleming CA et al (2022) Surgical trainee experience and opinion of robotic surgery in surgical training and vision for the future: a snapshot study of pan-specialty surgical trainees. J Robot Surg 16:1073–1082 Bostrom MPG, Lewis KO, Berger G (2021) Orthopaedic Electronic Learning Graduate Medical Education Consensus Working, G. The Role of Electronic Learning in Orthopaedic Graduate Medical Training: A Consensus From Leaders in Orthopaedic Training Programs. J Am Acad Orthop Surg 29:317–325 Pell G, Fuller R, Homer M, Roberts T (2012) Is short-term remediation after OSCE failure sustained? A retrospective analysis of the longitudinal attainment of underperforming students in OSCE assessments. Med Teach 34:146–150 Mills G, Ruzycki SM, Sabourin J, Dance E (2021) Experiences of breastfeeding among women residents in Alberta: a cross-sectional survey. Postgrad Med 133:42–47 Stentz NC, Griffith KA, Perkins E, Jones RD, Jagsi R (2016) Fertility and Childbearing Among American Female Physicians. J Womens Health (Larchmt) 25:1059–1065 Rangel EL et al (2021) Incidence of Infertility and Pregnancy Complications in US Female Surgeons. JAMA Surg 156:905–915 Hashemi SB, Tan F, Kenneth; and, Bain CA (2019) Addressing Cognitive Load in Training on Electronic Medical Record Systems. ACIS 2019 Proceedings 56 Singhal K et al (2023) Large language models encode clinical knowledge. Nature Aruta J, Almazan JU, Alamri MS, Adolfo CS, Gonzales F (2022) Measuring mental well-being among frontline nurses during the COVID-19 crisis: Evidence from Saudi Arabia. Curr Psychol, 1–11 Sewell JL, Boscardin CK, Young JQ, Ten Cate O (2016) O'Sullivan, P.S. Measuring cognitive load during procedural skills training with colonoscopy as an exemplar. Med Educ 50:682–692 Bansal A (2024) University of Bergen, Norway Bansal A et al (2024) Role of Ultrasound-Based Therapies in Cardiovascular Diseases. Struct Heart, 100349 Atkinson RC, Shiffrin RM, Human Memory (1968) A Proposed System and its Control Processes. in Psychology of Learning and Motivation , Vol. 2 (eds. Spence, K.W. & Spence, J.T.) 89–195Academic Press Additional Declarations The authors declare no competing interests. Supplementary Files 12.03.2026Additionalfile1searchstrategy.xlsx Additional file 1: Search strategy 12.03.2026Additionalfile2selectedarticles.xlsx Additional file 2: Included articles Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9108201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605293601,"identity":"9e69b93a-9d2b-4ef0-9e65-89fc13b0b79c","order_by":0,"name":"Amit Bansal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACNgbGBgbGHyAmc+MDBoMDhLXwMzA2NjD2gJiMzQZEaZEE2tHA2AbW0ibBwECEFoNrh9sfMPbY5cm3H2yr5im4w2DO3kBAy+1EoMN+JBcbnElsu81j8IzBsoeATRAtPcyJGxjAWg4zGNxIwK/FHqylrT5xfv/DtmKitEBsaTuc2HAjsY2ZaC0zEnuOJ2648bBZco7BMx6DMwT9kv7gw8cf1UCHJR/88ObPHTmD4w34tYABskt4iFA/CkbBKBgFo4AQAABLqFAQNSwklAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0681-932X","institution":"Department of Clinical Science, Faculty of Medicine, University of Bergen, Bergen, Norway","correspondingAuthor":true,"prefix":"","firstName":"Amit","middleName":"","lastName":"Bansal","suffix":""},{"id":605293602,"identity":"7af6c961-a941-4e8f-ae84-2a58963d0ee0","order_by":1,"name":"Rosalie Wing Yan Lai","email":"","orcid":"","institution":"Department of Strategy and Management, NHH Norwegian School of Economics, Bergen, Norway","correspondingAuthor":false,"prefix":"","firstName":"Rosalie","middleName":"Wing Yan","lastName":"Lai","suffix":""},{"id":605293603,"identity":"ed0026a0-004e-4983-bd43-7a10dcf8ae7e","order_by":2,"name":"Linn Liljeros","email":"","orcid":"","institution":"Fagskulen Vestland (Higher Vocational College), Norway","correspondingAuthor":false,"prefix":"","firstName":"Linn","middleName":"","lastName":"Liljeros","suffix":""},{"id":605293604,"identity":"b60110b0-ee15-470f-a951-84b978e40eef","order_by":3,"name":"Jagkirat Singh","email":"","orcid":"https://orcid.org/0009-0001-5712-4608","institution":"Department of Neurology, School of Medicine, Creighton University, Omaha, USA","correspondingAuthor":false,"prefix":"","firstName":"Jagkirat","middleName":"","lastName":"Singh","suffix":""},{"id":605293605,"identity":"a6d39964-75a7-485f-934d-88618bc93956","order_by":4,"name":"Silvana Bettiol","email":"","orcid":"https://orcid.org/0000-0002-4355-4498","institution":"Tasmanian School of Medicine, University of Tasmania, Hobart, Tasmania, Australia","correspondingAuthor":false,"prefix":"","firstName":"Silvana","middleName":"","lastName":"Bettiol","suffix":""},{"id":605293606,"identity":"a4ab3532-330b-47c6-8076-88a83f64fec2","order_by":5,"name":"Nhat Quang Le","email":"","orcid":"https://orcid.org/0000-0002-5199-2754","institution":"Department of Strategy and Management, NHH Norwegian School of Economics, Bergen, Norway","correspondingAuthor":false,"prefix":"","firstName":"Nhat","middleName":"Quang","lastName":"Le","suffix":""}],"badges":[],"createdAt":"2026-03-12 20:48:41","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9108201/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9108201/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104710965,"identity":"8f94b353-11e1-4d27-82fe-bfb6c51a5afe","added_by":"auto","created_at":"2026-03-16 10:14:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":426565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBehavioural and physiological response to stressors among healthcare workers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated with BioRender.com, 2026. Adapted based upon work by Tucak-Smajić, Rahić and McEwen\u003csup\u003e20-22\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/da356e6db322a5608a7cd45c.png"},{"id":104710917,"identity":"8d6342f7-427e-4ffa-8ca3-e2e5cfbf77a5","added_by":"auto","created_at":"2026-03-16 10:14:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":736333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA-ScR flowchart for searches, screening and full-text review.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRecords identified: 8,395 (Embase 110; MEDLINE 124; Web of Science 188; CINAHL 66; Google Scholar/hand‑searching 7,907 wherein first 500 records were considered after deduplication). Searches: 18-19 February 2025 (databases); 27 February 2026 (Google Scholar/hand‑searching). After deduplication and screening, 125 studies were included in the narrative synthesis. Inclusion required alignment to the eligibility frameworks and reporting of outcomes relating to well‑being, retention/intent‑to‑stay, or performance (skills, reasoning, errors, patient outcomes). A meta‑analysis was not planned.\u003c/p\u003e\n\u003cp\u003eAbbreviation: PRISMA-ScR, Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/61612de83ab98572b144902b.png"},{"id":104711014,"identity":"167c70e3-19d9-45cf-b84c-ac96306168bf","added_by":"auto","created_at":"2026-03-16 10:14:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential cognitive overload factors in healthcare settings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated with BioRender.com, 2026. Adapted based upon work by Atkinson, Howie, van Merrienboer, Sweller and others\u003csup\u003e16-19,55,147,166\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/c3f142823759f08d73ebb7a1.png"},{"id":104710990,"identity":"5d412b06-e571-47cd-b93b-1c07a4361461","added_by":"auto","created_at":"2026-03-16 10:14:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":491144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCognitive load management strategies in healthcare settings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated with BioRender.com, 2026. Adapted based upon work by Atkinson, Howie, van Merrienboer, Sweller and others\u003csup\u003e16-19,55,147,166\u003c/sup\u003e \u0026nbsp;\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/df5065d2a3eb50220e018281.png"},{"id":104710874,"identity":"a994256b-2282-4776-9590-7397617600cf","added_by":"auto","created_at":"2026-03-16 10:14:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":366377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential key actors for promoting wellbeing and excellence in healthcare settings.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCreated with BioRender.com, 2026.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/8d31e56acc188c6d8660214e.png"},{"id":104783227,"identity":"ac564954-c3c5-44de-920a-837a27b21c56","added_by":"auto","created_at":"2026-03-17 07:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3217610,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/956c6c17-9416-48e5-a3f0-ad094b3d8aa0.pdf"},{"id":104710996,"identity":"91c03711-e108-41de-8b6f-2553a057e5a6","added_by":"auto","created_at":"2026-03-16 10:14:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53057,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Search strategy\u003c/p\u003e","description":"","filename":"12.03.2026Additionalfile1searchstrategy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/962a03a588e668271b47f4d6.xlsx"},{"id":104711001,"identity":"9403f8eb-65a0-4cee-ba9b-544b7f0171ed","added_by":"auto","created_at":"2026-03-16 10:14:36","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54410,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Included articles\u003c/p\u003e","description":"","filename":"12.03.2026Additionalfile2selectedarticles.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9108201/v1/4cbbd3aed9ee8445b069b5b1.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCognitive load in clinical training: a scoping review of factors and strategies linked to well‑being, retention, and performance\u003c/p\u003e","fulltext":[{"header":"Summary","content":"\u003cp\u003eHealthcare workers face substantial cognitive load, with limited research on practical strategies to manage it and support clinical trainees' retention, well-being, and performance. Across diverse designs, higher perceived workload is often associated with poorer well‑being and intent to leave; effects on performance vary by task complexity and expertise. Managing intrinsic, extraneous, and germane load may be associated with better learning efficiency; links to well‑being and retention are suggested but heterogeneous across studies. Promising, but heterogeneously evaluated, strategies include deliberate practice, structured instructional supports (worked examples, schemas, mnemonics), situational awareness, reflection, and simulation. Organisational supports (e.g., protected time, leadership support, workload redesign) are associated with well-being and, in turn, support performance.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eClinical training requires rapid information processing, multi‑tasking, and high‑stakes decision‑making conditions that can impose substantial cognitive demands on healthcare workers (HCWs). The elevated stakes inherent in healthcare settings are often associated with emotional, cognitive and physical stresses, contributing to elevated burnout\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and suicide ideation/risk\u003csup\u003e2,3\u003c/sup\u003e in some cohorts. Estimates of burnout vary widely by cohort, instrument, and period; for example, a meta‑analysis reports\u0026thinsp;~\u0026thinsp;37% among medical students\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, while other cohorts show substantial heterogeneity (10\u0026ndash;77%)\u003csup\u003e5\u0026ndash;8\u003c/sup\u003e. Prevalences varies by cohort, instrument, and period; putative contributors include workload, responsibility, and exposure to morbidity and mortality on a regular basis.\u003c/p\u003e \u003cp\u003eHCW training has changed dramatically due to the coronavirus disease (COVID-19) pandemic, which unfortunately leads to even higher risks of physical isolation, burnout, and suicidal thoughts for medical trainees\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In a cross-sectional study, personal protective equipment (PPE) availability, staff training pre-redeployment, and provision of mental health support, were significantly associated with mental health in 41 countries\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This is likely related to higher risk for HCWs of being infected with respiratory viruses\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, a 2022 paper\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e reported that intense increase in burnout (63% in 2021 vs. 38% in 2020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decrease in satisfaction with work-life balance (46% in 2020 vs. 30% in 2021, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) occurred in US physicians. In the US\u003csup\u003e14\u003c/sup\u003e, 1 in 5 physicians and 2 in 5 nurses who worked in the COVID-19 response intend to leave their practice altogether in 2020. Burnout, fear of exposure, COVID-19-related anxiety/depression, and workload were all independently related to intent to reduce work hours or leave one\u0026rsquo;s practice within 1 year (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003csup\u003e14\u003c/sup\u003e. Additionally, redeployment to new roles without adequate training and lack of mental health support may increase the risk of burnout and suicidal ideation in HCWs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognitive load refers to the amount of intellectual effort required to process information or stressors due to demands on working memory storage and information processing\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Cognitive load theory distinguishing intrinsic (linked to the task itself), extraneous (external to the task or unnecessary for completing the task such as environment) and germane (to facilitate learning) loads\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. New information is processed by bounded working memory capacity and duration, with learning supported by schema formation in long-term memory for future use\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Allostatic load is the cumulative burden of chronic stress and life events. Practical stress-management approached are often advocated for normal allostatic response\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e); however, our review focuses on training design and system factors linked to cognitive load. Competence is the inherent state of possessing knowledge and cognitive skills to successfully perform a specific or range of tasks\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It is often difficult to determine, and different medical specialities have their specific criteria for evaluation. Certification (or licensing) is the method by which external organisations formally recognise a provider and provide them appropriate authorisation to manage patients, and tasks based the competence of the provider\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCognitive overload occurs when the overall cognitive load surpasses the working memory capacity of trainees\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. During training, cognitive load of HCWs can be high due to the complexity of the procedures, the amount of information that needs to be processed, and the stress of working in a high-pressure environment. Cognitive overload and low perceived value are associated with poorer well-being and training outcomes, with potential implications for patient safety\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Cognitive load management is described here as the proactive management of cognitive stressors to facilitate trainees\u0026rsquo; wellness and performance across the training period. Tokuno et al.\u003csup\u003e25\u003c/sup\u003e implied that cognitive overload is more likely in trainees performing more complex tasks and in high-fidelity environments and is more common among less experienced trainees.\u003c/p\u003e \u003cp\u003eWhile cognitive load theory offers valuable insights into learning design, existing research has primarily focused on specific educational settings or professions. This review aims to bridge this gap by examining the cognitive load factors and management strategies associated with outcomes in HCW training and practice. By exploring the interplay between cognitive load, well-being, retention, and performance in this under-researched domain, this review contributes to approaches for supporting HCWs. To reduce the likelihood of medical and surgical trainees leaving their profession and enhance their well-being and performance, it is crucial to develop strategies to help these trainees manage their cognitive load more efficiently. This review discusses cognitive overload and explores strategies for cognitive load management during HCW training and practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e A comprehensive plan for participant inclusion and exclusion, data screening, and collection was established. This plan, outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, used a population, concept and context (PCC), and population, intervention or exposure, comparison or control, and outcome (PICO) frameworks. Inclusion or exclusion of studies focused on relevant population (HCWs), intervention (cognitive load management strategies) and outcomes such as well-being (e.g., stress, burnout, anxiety), retention (e.g., job satisfaction and likelihood of continued working as a HCW), and performance (e.g., clinical skills, clinical reasoning, diagnostic accuracy, procedural competence, long-term application of learning, patient outcomes and error rates).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEligibility frameworks (PCC and PICO)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramework\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCC\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003eopulation\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003eoncept\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003eontext\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthcare workers/trainees (students, residents, fellows, nurses, allied health)\u003c/p\u003e \u003cp\u003eCognitive load factors and management strategies strategies)\u003c/p\u003e \u003cp\u003eClinical pedagogy. We included empirical studies (experimental, quasi‑experimental, observational, qualitative) and, a priori for scoping breadth, select non‑empirical sources (protocols/consensus/tutorials) when they explicitly addressed cognitive load therapy‑aligned strategies or measurement. No date or language limits were applied.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePICO\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003eopulation\u003c/p\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e (Intervention/Exposure)\u003c/p\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e (Comparison)\u003c/p\u003e \u003cp\u003e\u003cb\u003eO\u003c/b\u003e (Outcome)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical trainees (e.g., medical students, residents, nursing students, allied health students, intern, fellow, consultant)\u003c/p\u003e \u003cp\u003eCognitive load management strategies\u003c/p\u003e \u003cp\u003eTraditional clinical training methods (i.e., clinical training without explicit cognitive load management strategies)\u003c/p\u003e \u003cp\u003eImproved clinical skills, reduced errors, enhanced knowledge retention, increased confidence, HCW well-being, decreased stress/burnout, improved patient outcomes, long-term competency and HCW retention (with validated instruments)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003ePCC population, concept, and context.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003ePICO population, intervention or exposure, comparison or control, and outcome.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResearch question: What cognitive load factors and management strategies are associated with improved well-being, retention, and performance among trainees in HCWs clinical training and practice?\u003c/p\u003e \u003cp\u003eReview procedure: We conducted a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, followed by narrative synthesis. A narrative synthesis was selected to provide a broad overview and explore the conceptual foundations and scope of cognitive load management in clinical training, rather than to produce a narrowly focused, quantifiable estimates.\u003c/p\u003e \u003cp\u003eRayyan-a web and mobile app for systematic reviews\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e was used for this review. A medical librarian refined the strategy and database selection (Embase, MEDLINE, Web of Science, and Cumulative Index to Nursing and Allied Health, CINAHL). Full strategies for each database are in Additional file 1, including Google Scholar and hand-searches. Two authors independently performed screening and full-text review of the databases, with conflicts resolved by discussion, based on: a) Records published up until 27 February 2026; b) population: clinical trainees or HCWs; c) focus: cognitive load factors or management strategies; d) outcomes: well-being, retention, and/or performance (skills, reasoning, errors, patient outcomes). Three co-authors charted data, corresponding to the columns in Additional file 2, and conflicts were resolved through discussions.\u003c/p\u003e \u003cp\u003eCritical appraisal: No formal risk‑of‑bias appraisal (and meta-analysis) was performed, consistent with scoping review objectives.\u003c/p\u003e \u003cp\u003eSynthesis: We grouped findings by cognitive load therapy construct (intrinsic/extraneous/germane) and outcome domain, using inductive thematic mapping.\u003c/p\u003e\u003cp\u003eRegistration: The scoping review was made pre-registered through the Open Science Framework (https://doi.org/10.17605/OSF.IO/VW9FN) on 26 November 2023, and last update on 27 February 2026. No deviations from the protocol occurred beyond updating Google Scholar and a hand‑search on 27 February 2026. Search strings, database additions and dates are in Additional file 1.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe search strategy resulted in 8,395 records. Systematic searches were executed in Embase (n\u0026thinsp;=\u0026thinsp;110), MEDLINE (n\u0026thinsp;=\u0026thinsp;124), Web of Science (n\u0026thinsp;=\u0026thinsp;188), and CINAHL (n\u0026thinsp;=\u0026thinsp;66), on 18\u0026ndash;19 February 2025. These were supplemented by Google Scholar and hand searches (n\u0026thinsp;=\u0026thinsp;7,907) were completed on 27 February 2026 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Google Scholar and hand-searches was limited to the first 500 records after deduplication. After deduplication, 813 records underwent title and abstract screening, and 306 records underwent full-text review. A total of 125 articles were included (Additional file 2; references \u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e16,57\u0026ndash;87\u003c/sup\u003e,\u003csup\u003e88\u0026minus;110\u003c/sup\u003e,\u003csup\u003e3,13,14,25,111\u0026ndash;129\u003c/sup\u003e,\u003csup\u003e18,26,130\u0026ndash;141\u003c/sup\u003e,\u003csup\u003e142\u0026minus;146\u003c/sup\u003e). We found studies were predominantly from the USA (45), followed by Canada (18), Australia (17), the UK (10), Germany (9), the Netherlands (7), and Denmark (4) by corresponding author institution. Other countries are represented to a lesser extent (see Additional file 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the subsequent sections, we investigate cognitive overload factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and strategies for managing cognitive overload, including chunk-based practice with feedback, metacognition, situation awareness, reflection practices, digital training: simulation and artificial intelligence (AI)-based technologies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCognitive load management strategies in clinical training\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManaging intrinsic load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreasing extraneous load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimizing germane load\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradually proceed from low- to high- complexity task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvide learners with worked solutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduce variability across tasks; prompt self‑explanations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProceed from low- to high- fidelity environments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePart-task preparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternate tasks on varying patient presentations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChunk or part-task training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoal-free tasks completion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncourage trainees to self-explain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduce task element interactivity by task repetition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvoid providing redundant and split information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreate links between prior learning with new knowledge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdapted based upon work by Atkinson, Howie, van Merrienboer, Sweller and others \u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eChunk-based practice and training duration\u003c/h3\u003e\n\u003cp\u003eOne important strategy for managing intrinsic load is the use of deliberate practice or repetition with focused feedback\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e,\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e,\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e, which may impact performance\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Deliberate practice involves breaking down complex tasks into smaller, manageable chunks and practicing each chunk exclusively (therefore, managing the intrinsic load) before integrating them into the whole task\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e. For instance, complex medical or surgical procedures can be segmented into discrete steps incorporating simplified task structures, allowing trainees to master each component before progressing to the full operation to enhance skill acquisition and retention\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Deliberate practice is a promising training method for procedures such as ultrasound-guided vascular access and thoracentesis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. This approach helps manage cognitive load by allowing trainees to focus on one aspect of the procedure at a time in a flexible manner, rather than trying to process all the information at once. Although exact amount of training needed to hone core skills is still unknown, multiple studies\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e,\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e,\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e\u003c/sup\u003e have indicated that concise, focused training sessions ranging from several hours to multiple sessions can efficiently train HCWs in hands-on tasks. The gradual incorporation of terminally branching schemes\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e into training programmes (i.e., adopting a step-by-step learning approach) may be associated with enhanced diagnostic accuracy, minimised perceptual errors, and reduced cognitive load; thereby, improving clinical decision-making. Furthermore, Yeo and Romero\u003csup\u003e\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e recommend three processes for acquiring expert sonography skills: extensive training, deliberate practice, and performance evaluation/feedback.\u003c/p\u003e\n\u003ch3\u003eMetacognition\u003c/h3\u003e\n\u003cp\u003eMetacognition is described as \u0026ldquo;thinking about thinking\u0026rdquo; and is the ability to be aware of one's own thinking processes and to use this awareness to improve learning\u003csup\u003e\u003cspan additionalcitationids=\"CR150\" citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e\u003c/sup\u003e. It involves monitoring one's understanding of own thought processes, identifying one's strengths and weaknesses, and selecting appropriate strategies for learning\u003csup\u003e\u003cspan additionalcitationids=\"CR150\" citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e\u003c/sup\u003e. Metacognition is an important skill for both trainees and experts, and it can be improved with practice. Metacognition is not directly tied to either self-reflection or a supervised process, although it can be related to both. Self-reflection constitutes a specific form of metacognition, where individuals actively contemplate their own thoughts, feelings, and behaviours. Supervised processes, on the other hand, can involve external guidance in shaping one's thinking. For example, a senior physician might ask questions to help trainee understand their own thought process. While metacognition can be effectively utilised within supervised settings, it is not inherently dependent on them. The cognitive errors can be caused by several factors\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, including personal, workplace, and patient-related issues, which may impact trainees\u0026rsquo; confidence and well-being. The metacognitive processes could be tailored against factors associated with cognitive errors. Employing metacognitive strategies, such as structured learning scaffolds and reflective practice on personal experiences, could potentially promote anxiety management skills within nursing practitioners\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSituation awareness\u003c/h3\u003e\n\u003cp\u003eChen et al. suggested that situational awareness is learnable and measurable task. They developed a surgical adaptation of a validated situational awareness assessment technique\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Situational awareness was divided into three domains: understanding (i.e., familiarity of operating room and role of different team members in the operating room), attentional demand (stability, complexity, and demand of operating room) and attentional supply (alertness, concentration, attention, and spare mental capacity). Chen et al. found students/clerks have lower baseline situational awareness scores and higher perceived complexity of a surgical procedure. Trainees\u0026rsquo; situational awareness was significantly boosted, particularly in the understanding domain, after the clerkship period (up to 18 weeks). However, attentional demand and supply might require longer periods of clerkship training\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eReflection practices and leveraging prior knowledge\u003c/h3\u003e\n\u003cp\u003eMorrill et al. studied prevalence and types of operative reflection practices among surgical residents in a US hospital setting\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Three primary methods of post-operative reflection were mental, written, and oral. Among these, mental reflection was the most common method including step-by-step mental rehearsal, internal narration, and question series. For example, pre-operative 3D segmentation was reported to enhance the accuracy of surgical planning and reduced the time required while optimising the cognitive load\u003csup\u003e\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e,\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe combination of demographic and cognitive load variables produced more sensitive metrics to predict ultrasound simulator performance in a proof-of-principle study from the University of Toronto\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In terms of acquiring ultrasound images, seasoned users fared better than novices. The two groups' interpretations of the ultrasound images were similar. The degree of training, previous ultrasound training, and cognitive load all predicted the performance of ultrasound image capture\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A nationwide Australian cross-sectional survey revealed five key factors that influence pedagogical approaches to teaching ultrasound scanning skills, including limited protected teaching time, perceived skill complexity, learner skill level and credentials, and avoiding overwhelming the learner, and patient well-being and willingness to be scanned\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDigital training: simulation\u003c/h2\u003e \u003cp\u003eDuring a simulation-based resuscitation programme\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, trainees who used preparatory online courses saw changes in cognitive load that were consistent with cognitive optimisation, which may lead to subsequent superior performance. Preclinical simulation may be associated with student performance, reduced clinical staff burden, and improved patient safety during early ultrasound training\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Similarly, one month after ultrasound simulation training\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, students showed a notable transfer of acquired ultrasound skills, attaining an average score of at least 3 out of 5, indicating advanced beginner proficiency in both complex psychomotor and patient-sonographer communication skills.\u003c/p\u003e \u003cp\u003eAccess to robotic platforms during training varies across systems. Robotic surgery began in the US\u003csup\u003e152\u003c/sup\u003e in the year 2000 and in Australia in 2003\u003csup\u003e153\u003c/sup\u003e. Surgical trainees in Australia\u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e\u003c/sup\u003e and the UK\u003csup\u003e154\u003c/sup\u003e spend seven years training; exposure can be inconsistent in some public settings. Some studies reported that virtual reality simulation training for mastoidectomy may reduce cognitive load compared to traditional cadaveric dissection for novices, underscoring the need to align modality, task complexity, and learner level\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAI-based technologies\u003c/h3\u003e\n\u003cp\u003eBostrom\u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e\u003c/sup\u003e et al. identified several shortcomings in current training methods (a consensus from the US leaders in Orthopaedic training programs). Firstly, training programs are not fully exploiting available technologies and innovations to enhance the learning experience. Secondly, trainees are often using electronic resources inappropriately, either without guidance or in the absence of a structured electronic training program. Examples of potentially inappropriate electronic resources for medical and surgical trainees include unreliable websites, educational websites authored by medical practitioners but lacking peer review, and resources of low quality. Thirdly, training programs often fail to provide individualized instruction to cater to the different learning paces and styles of trainees. Finally, formal electronic learning programs can make better use of trainees' time and resources compared to informal learning methods. In essence, Bostrom\u003csup\u003e\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e\u003c/sup\u003e et al. advocated for a more modern and effective approach to training that incorporates technological advancements, structured electronic learning programs, and individualized instruction to optimize the learning experience for all trainees. Interestingly, Turkish medical students perceived that while AI-based technologies could assist physicians\u0026rsquo; access to information (86%) and reduce errors (71%), many of them (45%) were worried about the possible of unemployment due to AI-based tools\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. An AI-derived knowledge recommender for radiology education has the capacity to decrease reporting times, enhance diagnostic accuracy, and alleviate overall workload and mental strain for radiology trainees\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Ambient AI platforms have also been reported to reduce documentation burdens and self-reported cognitive load among HCWs; trainee-specific effects require further study\u003csup\u003e\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e,\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eWorkplace environment and demonstrable outcomes\u003c/h3\u003e\n\u003cp\u003eThe evidence suggests that feeling valued by one's organisation may be associated with several positive outcomes\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, including reduced burnout, improved job satisfaction, and increased patient satisfaction. A survey from the US found that feeling valued by one's organisation was associated with lower odds of reducing hours or intending to leave\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy synthesising cognitive load factors and management strategies, this review provides a framework for enhancing trainee well-being and professional performance. Our findings indicate that elevated cognitive load is implicated in diminished HCW retention and well-being, correlating with heightened stress and burnout, thereby increasing turnover and reducing job satisfaction \u003csup\u003e1\u0026ndash;3,5\u0026minus;8\u003c/sup\u003e. The relationship between cognitive load and performance is more complex with a marginal positive association, suggesting optimal cognitive load may enhance performance\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e; however, it is plausible that cognitive overload can impair performance. Trainees should be informed of the risks due to high allostatic load, taking precautions, and being prepared for the unexpected\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Existing evidence shows that some groups may face additional competing demands that can contribute to cognitive overload. For example, poorly performing students, who may need personalised and research-led interventions after clinical examination failure\u003csup\u003e\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e\u003c/sup\u003e, or female trainees and physicians who may experience additional competing demands, such as breastfeeding at work and pregnancy related complications\u003csup\u003e\u003cspan additionalcitationids=\"CR158\" citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e\u003c/sup\u003e. Cognitive load theory principles provide a coherent lens to interpret mixed performance finding across modalities and learner levels\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e. These strategies can help trainees cope with the sudden changes in demands or cognitive load and enhance their learning and performance.\u003c/p\u003e \u003cp\u003eDeliberate practice is characterised by repetition, feedback, and progressive refinement\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e,\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e and enables learners to systematically and efficiently develop both technical and cognitive competencies\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. When combined with the streamlining of procedures from complex to simple, deliberate practice enhances skill retention, may be associated with reduced cognitive overload, and improvement of overall performance in clinical tasks\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e,\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e. Our results highlight that deliberate practice, which is goal-oriented, sequential, and tailored to the learner\u0026rsquo;s level, can help trainees adjust their approach and improve their performance\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e. Knowledge must be set up in your mind in a flexible, accessible manner that is predicated on a sizable and connected body of clinical cases so that HCWs can automatically recognise patterns based upon past experiences and commence strategies to systematically and logically analyse data to solve clinical cases. For example, rash, tiredness, and restless legs may seem unrelated at first until a HCW consider gastrointestinal causes, which may imply coeliac disease with secondary anaemia. Research indicates that learners benefit from a stepwise, scaffolded approach, transitioning from low-fidelity to high-fidelity simulations and gradually increasing task complexity as competency improves\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. Streamlining procedures for reduction of extraneous cognitive load include use of goal-free tasks, worked examples, and completion problems\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that distributed practice results in superior retention of psychomotor skills compared to massed practice (all practices performed in 1 day), as regular reinforcement facilitates the gradual consolidation of both motor and cognitive components\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Time-distributed practice consistently outperforms massed practice, especially for complex psychomotor skills, as it enables learners to refine their performance over multiple sessions rather than relying on an intensive, single-session approach\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEffective medical education frequently relies on structured instructional frameworks incorporating mnemonics, case-based learning, and modular, interactive educational designs to enhance skill development and knowledge retention. Interactive educational designs can integrate simulation-based learning, spaced learning, and test-enhanced learning methodologies to optimise long-term retention and cognitive efficiency\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e,\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. Early integration of clinical reasoning instruction in medical school can improve clinical performance by familiarising students with diagnostic frameworks, structured decision-making, and the real-world application of knowledge\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This approach not only strengthens clinical interpretation skills but also enhances confidence and preparedness in patient care settings. However, increasing instructional authenticity may not consistently improve clinical reasoning performance across all outcome measures and subject areas\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCase-based learning (CBL) is a highly effective instructional method that improves diagnostic accuracy and medical interpretation skills\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e. Research indicates that the use of CBL in combination with expert-generated schemas significantly enhances clinical interpretation, particularly in complex areas such as ECG rhythm analysis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. By engaging with realistic patient scenarios, students can develop a systematic approach to problem-solving, reinforcing both theoretical knowledge and practical application.\u003c/p\u003e \u003cp\u003eSituational awareness is important for managing cognitive load via optimising germane load and honing procedural skills\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e,\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e\u003c/sup\u003e. Team-based learning, by harnessing situational awareness, can support cognitive load by allowing trainees to share the load of learning new information and skills. Medical and surgical trainees are usually intrinsically motivated and can monitor their cognitive processes in action (e.g., self-reflect for biases or errors in their reasoning to amend them) by optimizing their germane load\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e\u003c/sup\u003e. By reviewing the recorded training sessions (self-assessment), HCWs can address procedures/techniques where they may have struggled or made errors to become more proficient in the respective techniques. During reflective training, residents can review their interactions with other team members and identify areas where they can improve their communication and teamwork skills. Prior self-directed and experiential learning (such as surgical observership or volunteering) makes the trainee comfortable with the operating room environment. Prior knowledge is important in enhancing the efficiency of learning, alleviating cognitive load, and optimising performance across diverse domains within medical training\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e,\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u003c/sup\u003e. Individuals with extensive expertise and prior experience exhibit superior performance in skill-based tasks, such as the acquisition of ultrasound images and the management of airways\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Medical training should be designed to effectively manage cognitive demand in accordance with learners' levels of expertise\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. Novices and experts process information in distinct ways, necessitating tailored instructional strategies to optimise learning, enhance performance, and mitigate cognitive overload. While novices benefit from structured guidance and immediate feedback within controlled environments, experts derive greater value from challenging, error-based learning and opportunities for delayed reflection to refine their decision-making capabilities\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Situational learning is a subjective measure, and triangulation with objective techniques may strengthen inferences.\u003c/p\u003e \u003cp\u003eSimulation training provides a safe environment for trainees to practice history taking, physical examination, surgical procedures and develop the processes and strategies of clinical reasoning without the risk of harming patients. By allowing trainees to practice in a controlled setting, simulation training may reduce the cognitive load associated with patient encounters or when performing surgery for the first few times, which can eventually help them become more confident and competent in their abilities. Despite this, the simulated learning environment presents a complex and cognitively demanding scenario, necessitating careful consideration of the learner's prior experience in managing cognitive load\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Simulation and prior clinical observerships may further enhance trainees\u0026rsquo; performance through harnessing situational awareness, along with deliberate practice and structured micro- or bio-breaks\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. The use of simulated patient encounters (or cases) and simulation-based training for procedural skills (e.g., ultrasound\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e) during the pre-clinical years or in early specialist training period\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e may accelerate early skill acquisition before clinical application; effects on training duration are unclear\u003csup\u003e\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e\u003c/sup\u003e. Simulation can help to manage extraneous cognitive load\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Pedagogical strategies incorporating time-distributed practice and virtual reality simulation demonstrate potentially fostering the sustained retention and reinforcement of complex psychomotor skills, notably those essential for the performance of mastoidectomy\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Although more cognitively demanding and time-intensive, students reported gaining a deeper understanding of complex tasks within simulated clinical immersion compared to simpler tasks. These experiences pose additional challenges to clinical reasoning, ultimately offering a more enriching and valuable learning experience from the students' perspective\u003csup\u003e\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e. Moreover, simulation-based deliberate practice in procedures such as airway management and central venous catheterisation may be associated with lower error rates in some procedural settings\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. By integrating simulator-based training with real-time feedback, trainees can likely refine their skills in low-risk environments before applying them in clinical settings\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDigital training (e.g., online medical courses and surgical simulations) may enhance trainees\u0026rsquo; situational awareness and optimise germane load\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e,\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e,\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u003c/sup\u003e. One important benefit of digital learning is the opportunity to provide students with immediate feedback and/or adapt the learning content to the learning ability of students. For example, some students might get exhausted faster than the others and the same student might also have different learning ability over days. As such customising these learning processes to make it adaptive and personally relevant would make it less cognitively intense. Cognitive load may contribute to simulation-based learning failures\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and further research is needed to determine how this understanding should inform simulation design. Integrating cognitive load measures into ongoing simulation research could quantify the theory's relevance to our field and provide an additional framework for interpreting emerging findings\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. AI-based platforms may support physicians and trainees who are struggling with time management due to documentation requirements\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e,\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u003c/sup\u003e via summarising individual electronic health records. However, large language models encoding clinical knowledge could also trigger unwarranted stress of misdiagnoses or unnecessary interventions\u003csup\u003e\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExternal factors such as information retrieval, interruptions, reporting software, and time pressure may contribute to extraneous load especially in cardiology trainees\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Shared workspace, access to attendings, and availability of final reports foster germane load\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Creating a positive learning environment may support HCW training, as it helps to enhance learning outcomes and improves the overall quality\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Physicians and medical students are more likely to make mistakes if they are hungry, angry, late, or tired\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Another key strategy for mitigating this is implementing safety breaks, which address both physical, cognitive and emotional depletion. Simple strategies such as hydration reminders, structured rest periods, and workplace policies that prioritise staff well-being can likely improve performance and job satisfaction\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHCWs who experience emotional exhaustion, detachment from work (depersonalisation), and a diminished sense of personal accomplishment, may have higher attrition rates and a potentially decline in the quality of patient care\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Workshops and interventions focusing on workload management, mindfulness, and structured breaks are important in creating sustainable learning environments and enhancing resilience among HCWs and students\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Addressing these challenges through targeted strategies can potentially mitigate burnout, reduce stress-related errors, and improve overall job satisfaction\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. It is important to measure baseline values and monitor the state of HCW well-being to identify at risk HCWs, and likely support them with a certified physician assistant, trained peer team and mental health experts\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. There are several ways to measure HCW well-being. Some common measures include self-reported surveys and biomarkers to assess the physical and psychological effects of stress. These surveys\u003csup\u003e\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e\u003c/sup\u003e ask HCWs about their levels of stress, anxiety, depression, and other mental health problems. Likewise, it could be useful to measure cognitive load during procedural skill training \u003csup\u003e\u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e\u003c/sup\u003e to better manage cognitive load and stresses in training period. It's important to prioritise evidence-based surveys and technologies tailored to the local training context.\u003c/p\u003e \u003cp\u003eThe HCW mental health is complex and requires a comprehensive approach involving stakeholders at all levels of the healthcare system. At the individual level, junior HCWs can learn adaptive coping skills through cognitive behavioural therapy and mindfulness techniques\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e, and they should be provided with specialised mental health services\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Senior HCWs can reinforce adaptive coping skills in their junior colleagues\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Retiring HCWs should be given appropriate preparation for retirement and be offered ongoing roles in teaching and training\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. At the institutional level, medical schools can teach students effective coping skills and normalize help-seeking behaviour\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Hospitals and health systems can support administrative burdens, ensure workplace autonomy, and provide job security for HCWs\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, by focusing on interventions such as leadership development, strategic recruitment, and adequate staffing and resources\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Professional colleges and external regulators can consider implementing individually focused interventions such as offer personnel selection for supervisory roles, mandatory reporting rules, flexible leave arrangements, and work hours and shifts, where feasible\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The consensus statement\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e from New Zealand and Australia recommends that medical schools promote student well-being through peer support, stress management, mental health promotion, physical health promotion, and a safe and health-promoting learning environment. In addition, implementing policies that aim to increase financial rewards, offer opportunities for professional growth, promote flexibility, and build employees\u0026rsquo; strong ties to the community may help to enhance HCW retention\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInstitutional interventions can complement individual coping strategies and help HCWs manage stress more effectively\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. HCWs may benefit from relaxation techniques (such as mindfulness-based practices, yoga, meditation, and acupuncture) and having a positive mindset\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Burnout-awareness workshops equip HCWs with the tools to recognise early warning signs and adopt preventive measures, such as maintaining a work-life balance, utilising peer support systems, and adjusting workloads\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. High levels of stress in students, arising from uncertainties in medical management, can negatively impact their cognitive processes\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. However, familiarity with the practice setting, anticipation of challenges, debriefing sessions, the provision of a safe learning environment, and future training opportunities can help students manage stress effectively, thereby transforming simulations into positive learning experiences\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e,\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e,\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e\u003c/sup\u003e. Consequently, by integrating these approaches into professional development programmes, healthcare institutions can prevent burnout and improve workforce retention. Mental rehearsal practice may be associated with improved procedural performance\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e, likely reducing stress by enabling individuals to visualise complex procedures or decision-making scenarios. Mindfulness-based interventions have demonstrated efficacy in reducing burnout by enhancing attentional control, emotional regulation, and stress resilience\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e,\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Research indicates that mindfulness training diminishes medical errors, improves decision-making under pressure, and promotes overall well-being\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. These interventions can be seamlessly incorporated into daily routines or delivered through structured formats such as workshops, guided reflection sessions, and stress management programmes, thereby fostering long-term resilience among healthcare professionals. When trainees and medics use their skills and knowledge to make a positive difference in the lives of others, they may find happiness and fulfilling life\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis review is not without its limitations. The task of interweaving cognitive load, well-being, and performance into a cohesive review proved challenging. We sought to reduce retrieval bias by including four databases and Google Scholar/hand‑searches; however, publication bias cannot be excluded. There is a possibility that we may have unintentionally overlooked relevant studies that did not employ specific keywords associated with cognitive load and well-being. This oversight could result in a less comprehensive grasp of the existing evidence, potentially skewing our understanding of the relationship between the factors identified and the success of trainees.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe cognitive load theory inspired strategies may support HCWs to enhance their well-being, learning and performance, by using deliberate practice, instructional frameworks (structured learning, mnemonics, and case-based learning), situation awareness, reflection practices and digital training. Resource levels, staffing models, case-mix, and sociocultural factors likely moderate both cognitive load and intervention effectiveness, although standardised measurement and trainee‑centred outcomes (well‑being/retention) remain under‑studied. To ensure a more global perspective and help reduce avoidable sources of cognitive overload and support well-being, further research on cognitive load factors and management strategies is necessary, particularly from underrepresented regions such as Asia, Africa, South America, parts of Eastern Europe, the Middle East, and Oceania beyond Australia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: AB and JS developed the concept of the paper. AB and LL performed literature search using four databases, Google Scholar, hand searching; and screening and full-text review using Rayyan Review Management Platform, shown in the additional file 1. AB, RL and SB extracted the study data shown in the additional file 2. AB wrote the original manuscript and created the figures. AB, RL, LL, SB and NQL provided critical input, revisions, and contributed to the interpretation of the reviewed findings. AB amended the original draft. All authors reviewed and contributed to the work presented. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: \u003c/strong\u003eWe thank and appreciate Malik Beglerovic, librarian at the University of Bergen, Norway, for helping us in refining and updating the search strategy. AB extends thanks to colleagues at the University of Melbourne\u0026rsquo;s Teaching Certificate program, University of Bergen, and worldwide for their support\u003csup\u003e164,165\u003c/sup\u003e.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e AB received funding from the University of Bergen, The National Graduate School in Infection Biology and Antimicrobials (or IBA) and Pasteur legatet \u0026amp; Thj\u0026oslash;tta\u0026rsquo;s legat, University of Oslo, Norway [101563]. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003ePotential conflicts of interest: \u003c/strong\u003eAuthors report no potential conflict of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: Ethics approval was not required for the preparation of this article. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Data sharing is not applicable to this article as no new datasets were generated or analysed during the current study. \u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSletta C, Tyssen R, Lovseth LT (2019) Change in subjective well-being over 20 years at two Norwegian medical schools and factors linked to well-being today: a survey. BMC Med Educ 19:45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogel L (2019) US doctors call for tracking of suicides among medical trainees. CMAJ 191:E26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStacey A et al (2022) Warning: medical education is hazardous to your mental health. Medical students should make an informed decision to begin and continue training. Can Med Educ J 13:39\u0026ndash;44\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmutairi H et al (2022) Prevalence of burnout in medical students: A systematic review and meta-analysis. Int J Soc Psychiatry 68:1157\u0026ndash;1170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang E, Eddins-Folensbee F, Coverdale J (2012) Survey of the prevalence of burnout, stress, depression, and the use of supports by medical students at one school. Acad Psychiatry 36:177\u0026ndash;182\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltannir Y et al (2019) Assessment of burnout in medical undergraduate students in Riyadh, Saudi Arabia. BMC Med Educ 19:34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbosa ML et al (2018) Burnout Prevalence and Associated Factors Among Brazilian Medical Students. Clin Pract Epidemiol Ment Health 14:188\u0026ndash;195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuaddi MA et al (2023) Assessment of Medical Students Burnout during COVID-19 Pandemic. Int J Environ Res Public Health 20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede la Vega Sanchez D, Irigoyen-Otinano M, Carballo JJ, Guija JA, Giner L (2023) Suicidal thoughts and burnout among physicians during the first wave of the COVID-19 pandemic in Spain. Psychiatry Res 321:115057\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhajuria A et al (2021) Workplace factors associated with mental health of healthcare workers during the COVID-19 pandemic: an international cross-sectional study. BMC Health Serv Res 21:262\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrieu MC et al (2021) SARS-CoV-2-Specific Neutralizing Antibody Responses in Norwegian Health Care Workers After the First Wave of COVID-19 Pandemic: A Prospective Cohort Study. J Infect Dis 223:589\u0026ndash;599\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal A et al (2023) Risk assessment and antibody responses to SARS-CoV-2 in healthcare workers. Front Public Health 11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanafelt TD et al (2022) Changes in Burnout and Satisfaction With Work-Life Integration in Physicians During the First 2 Years of the COVID-19 Pandemic. Mayo Clin Proc 97:2248\u0026ndash;2258\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinsky CA, Brown RL, Stillman MJ, Linzer M (2021) COVID-Related Stress and Work Intentions in a Sample of US Health Care Workers. Mayo Clin Proc Innov Qual Outcomes 5:1165\u0026ndash;1173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnotz W, Kurschner C (2007) A reconsideration of cognitive load theory. Educ Psychol Rev 19:469\u0026ndash;508\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowie EE et al (2023) Cognitive Load Management: An Invaluable Tool for Safe and Effective Surgical Training. J Surg Educ 80:311\u0026ndash;322\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweller J, van Merri\u0026euml;nboer JJG, Paas F (2019) Cognitive Architecture and Instructional Design: 20 Years Later. Educ Psychol Rev 31:261\u0026ndash;292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Merrienboer JJ, Sweller J (2010) Cognitive load theory in health professional education: design principles and strategies. Med Educ 44:85\u0026ndash;93\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweller J, van Merrienboer JJG, Paas FG (1998) W.C. Cognitive Architecture and Instructional Design. Educ Psychol Rev 10:251\u0026ndash;296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcEwen BS (2007) Physiology and neurobiology of stress and adaptation: central role of the brain. Physiol Rev 87:873\u0026ndash;904\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahić O, Tucak A, Sirbubalo M, Hindija L, Hadžiabdić J (2021) Antihypertensives\u0026rsquo; Rock around the Clock. J 4:62\u0026ndash;81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTucak-Smajić A (2023) Stress Response and Development of Allostatic Load\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoni N, Arntfield R, Kory P (2014) \u003cem\u003ePoint of Care Ultrasound\u003c/em\u003eS aunders\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerjets P, Scheiter K, Cierniak G (2009) The Scientific Value of Cognitive Load Theory: A Research Agenda Based on the Structuralist View of Theories. Educ Psychol Rev 21:43\u0026ndash;54\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTokuno J, Carver TE, Fried GM (2023) Measurement and Management of Cognitive Load in Surgical Education: A Narrative Review. J Surg Educ 80:208\u0026ndash;215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest CP, Dyrbye LN, Shanafelt TD (2018) Physician burnout: contributors, consequences and solutions. J Intern Med 283:516\u0026ndash;529\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTricco AC et al (2018) PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 169:467\u0026ndash;473\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuzzani M, Hammady H, Fedorowicz Z, Elmagarmid A (2016) Rayyan\u0026mdash;a web and mobile app for systematic reviews. Syst Reviews 5:210\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAalioui L, Gouzi F, Tricot A (2022) \u003cem\u003eReducing cognitive load during video lectures in physiology with eye movement modeling and pauses: a randomized controlled study\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAldekhyl S, Cavalcanti RB, Naismith LM (2018) Cognitive load predicts point-of-care ultrasound simulator performance. Perspect Med Educ 7:23\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander LF et al (2023) Ultrasound Simulation Training for Radiology Residents-Curriculum Design and Implementation. J Ultrasound Med 42:777\u0026ndash;790\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbardekar AP, Rosero EB, Bhoja, Ravi, Green J, Rebal BA, Minhajuddin, Abu (2019) \u0026hellip; \u003cem\u003eA Randomized Controlled Trial Comparing Learners' Decision-making, Anxiety, and Task Load During a Simulated Airway Crisis Using Two Difficult Airway Aids\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmey L, Donald KJ, Teodorczuk A (2017) Teaching clinical reasoning to medical students. Br J Hosp Med (Lond) 78:399\u0026ndash;401\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen SA, Mikkelsen PT, Konge L, Caye-Thomasen P, Sorensen MS (2016) Cognitive Load in Mastoidectomy Skills Training: Virtual Reality Simulation and Traditional Dissection Compared. J Surg Educ 73:45\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen SAW, Konge, Lars C-T (2016) Per, Sorensen, Mads Solvsten. Retent Mastoidectomy Skills After Virtual Real Simul Train\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersen SAW, Mikkelsen P, Trier (2016) Konge, Lars, Cay\u0026eacute;-Thomasen, Per, S\u0026oslash;rensen. \u0026hellip; Cogn load Distrib massed Pract virtual Real mastoidectomy Simul\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalslev T, Muijtjens A, de Grave W, Awneh H, van Merrienboer J (2021) How isolation key Inform allowing clarifying questions may improve Inform Qual Diagn accuracy case handover paediatrics\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarsuk JH, Ahya SN, Cohen ER, McGaghie WC, Wayne DB (2009) Mastery learning of temporary hemodialysis catheter insertion by nephrology fellows using simulation technology and deliberate practice. Am J Kidney Dis 54:70\u0026ndash;76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarsuk JH, McGaghie WC, Cohen ER, O'Leary KJ, Wayne DB (2009) Simulation-based mastery learning reduces complications during central venous catheter insertion in a medical intensive care unit. Crit Care Med 37:2697\u0026ndash;2701\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaverstock A, Finlay F (2019) Take a break: HALT-are you Hungry, Angry, Late or Tired? Arch Dis Child Educ Pract Ed 104:200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehrens C, Dolmans, Diana HJM, Leppink, Jimmie, Gormley GJ (2018) Driessen, Erik W. \u003cem\u003eWard round simulation in final year medical students: Does it promote students learning?\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellstedt M (2024) Holtrup, Adrian, Otto, Nils, Berndt, Markus, Scherff, Aline Doreen, Papan, C\u0026hellip; \u003cem\u003eGaze cueing improves pattern recognition of histology learners\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen Awadh A, Clark J, Clowry G, Keenan ID (2022) \u003cem\u003eMultimodal Three-Dimensional Visualization Enhances Novice Learner Interpretation of Basic Cross-Sectional Anatomy\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirt J, Moore E, Cowling M (2017) \u003cem\u003eImproving paramedic distance education through mobile mixed reality simulation\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlissett S, Cavalcanti R, Sibbald M (2015) \u003cem\u003eECG rhythm analysis with expert and learner-generated schemas in novice learners\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlissett S, Morrison D, McCarty D, Sibbald M (2017) \u003cem\u003eShould learners reason one step at a time? A randomised trial of two diagnostic scheme designs\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlissett S, Rodriguez S, Qasim A (2021) O'Sullivan, P. Learning Echocardiography in the Workplace: A Cognitive Load Perspective. Acad Med 96:441\u0026ndash;448\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBostrom M, Lewis KO, Berger G, Orthopaedic Elect Learning Grad Me (2021). \u003cem\u003eThe Role of Electronic Learning in Orthopaedic Graduate Medical Training: A Consensus From Leaders in Orthopaedic Training Programs\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowman A, Reid D, Bobby Harreveld R, Lawson C (2021) Evaluation of students' clinical performance post-simulation training. Radiography (Lond) 27:404\u0026ndash;413\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrzezinski M, Kukreja, Jasleen M (2019) John D. \u003cem\u003eTime-efficient, goal-directed, and evidence-based teaching in the ICU\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JJ, Gompers A, Evenson A, James BC, Royce C (2023) Surgical Adaptation of the Situation Awareness Rating Technique (S-SART): Assessing Situational Awareness Among Medical Students. J Surg Educ 80:216\u0026ndash;227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi-Lundberg DL, Cuellar WA, Williams, Anne-Marie M (2016) \u003cem\u003eOnline dissection audio-visual resources for human anatomy: Undergraduate medical students' usage and learning outcomes\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCivaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A (2022) Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ 22:772\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClarke K, Alim A-MA, Nimer L, Hatfield A, Sabharwal E, Syed S (2024) S. \u003cem\u003eUsing Mixed Reality Simulation to Improve Junior Medical Trainees' Preparedness to Manage High-Acuity Trauma\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen C, Pignata S, Bezak E, Tie M, Childs J (2023) Workplace interventions to improve well-being and reduce burnout for nurses, physicians and allied healthcare professionals: a systematic review. BMJ Open 13:e071203\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneral Practice Supervisors Australia (GPSA). Teaching clinical reasoning (Teaching guide) (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanboise B, Italiya BS, Richman S (2022) P. \u003cem\u003e254 Does Video Pre-Briefing Reduce Cognitive Load During a Simulated ACLS Scenario?\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDankbaar, M., Alsma, Jelmer, Jansen, Els, Merrienboer, Jeroen, Saase, Jan, Schuit,Stephanie, D\u0026hellip; \u003cem\u003eAn experimental study on the effects of a simulation game on students' clinical cognitive skills and motivation\u003c/em\u003e, (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonato AA, Kaliyadan AG, Wasser T (2014) \u003cem\u003eSelf-Directed Study Using MP3 Players to Improve Auscultation Proficiency of Physicians: A Randomized, Controlled Trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubert CJ (2013) \u003cem\u003eA mindfulness model of emotion regulation in nursing students: Working memory capacity as a regulatory mechanism\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubovi I (2018) \u003cem\u003eDesigning for online computer-based clinical simulations: Evaluation of instructional approaches\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubovi I, Lee VR (2019) \u003cem\u003eInstructional support for learning with agent-based simulations: A tale of vicarious and guided exploration learning approaches\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncan DR, Morgenthaler TI, Ryu JH, Daniels CE (2009) Reducing iatrogenic risk in thoracentesis: establishing best practice via experiential training in a zero-risk environment. Chest 135:1315\u0026ndash;1320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEickelmann AK, Huwendiek WNJ (2021) S. \u003cem\u003eTeaching the technical performance of bronchoscopy to residents in a step-wise simulated approach: factors supporting learning and impacts on clinical work - a qualitative analysis\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes R, Malloy-Diniz LF, de Vasconcellos MC, Camargos PAM, Ibiapina C (2021) \u003cem\u003eAdding guidance to deliberate reflection improves medical student's diagnostic accuracy\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlynn S.G., P.R.S., Jena A.B., Staffa S.J., Kim S.Y., Clarke J.D., Pham I.V., Lukovits K.E., H\u0026hellip; \u003cem\u003eCoaching inexperienced clinicians before a high stakes medical procedure: randomized clinical trial\u003c/em\u003e, (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFontaine G, Cossette S, Dube GM-P, Veronique, Cote J (2020) \u003cem\u003eEffectiveness of a Theory- and Web-Based Adaptive Implementation Intervention on Nurses' and Nursing Students' Intentions to Provide Brief Counseling: Protocol for a Randomized Controlled Trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser, K., Huffman, James, Ma, Irene, Sobczak, Matthew, McIlwrick, Joanne, Wright,Bruce, M\u0026hellip; \u003cem\u003eThe emotional and cognitive impact of unexpected simulated patient death: a randomized controlled trial\u003c/em\u003e, (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser K, Teteris MI, Baxter E, Wright H, McLaughlin B (2012) K. \u003cem\u003eEmotion, cognitive load and learning outcomes during simulation training\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser KL, Ayres P, Sweller J (2015) Cognitive Load Theory for the Design of Medical Simulations. Simul Healthc 10:295\u0026ndash;307\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman MA, Has TL, Hampton P (2017) B.S. \u003cem\u003eThe impact of a postpartum rounding video module on medical student learning during the obstetrics and gynecology core clerkship\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Ceken BD (2022) C., Kow A.W.C. \u003cem\u003eMixed reality enhanced laparoscopic living donor liver transplant\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGerrard KM, Shepherd J, Sehdev R, Gangathimmaiah V, Nagle C (2023) Meeting the wellness needs of emergency department clinicians: A scoping review of interventions. Australas Emerg Care 26:169\u0026ndash;178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrangeia T, de Jorge B, Franci D, Santos TM, Setubal MSV, Schweller M, de Carvalho MA (2016) \u003cem\u003eCognitive Load and Self-Determination Theories Applied to E-Learning: Impact on Students' Participation and Academic Performance\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreville WJ, Dymond, Simon N (2016) Philip M. \u003cem\u003eThe student experience of applied equivalence-based instruction for neuroanatomy teaching\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroot F, Jonker G, Rinia M, ten Cate O, Hoff RG (2020) \u003cem\u003eSimulation at the Frontier of the Zone of Proximal Development: A Test in Acute Care for Inexperienced Learners\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuluma KZ, Brandl K (2023) \u003cem\u003eVirtual learning allows for adaptation of study strategies in a cohort of U.S. medical students\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez G, Lunsky IO, Van Heer S, Szulewski A, Chaplin T (2023) Cognitive load theory in action: e-learning modules improve performance in simulation-based education. A pilot study. CJEM 25:893\u0026ndash;901\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackett M, Proctor M (2018) \u003cem\u003eThe effect of autostereoscopic holograms on anatomical knowledge: a randomised trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaji, F.A., Cheung, Jeffrey J H, Woods, Nicole, Regehr, Glenn, de Ribaupierre, Sandrine,Dubr\u0026hellip; \u003cem\u003eThrive or overload? The effect of task complexity on novices' simulation-based learning\u003c/em\u003e, (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey SB et al (2021) Mental illness and suicide among physicians. Lancet 398:920\u0026ndash;930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashemi SB, Tan F, K.and, Bain CA (2019) \u003cem\u003eAddressing Cognitive Load in Training on Electronic Medical Record Systems\u003c/em\u003eACIS 2019 Proceedings. 56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeer, S.V., Cofie, Nicholas, Gutierrez, Gilmar, Upagupta, Chandak, Szulewski, Adam,Chaplin,\u0026hellip; \u003cem\u003eShaken and stirred: emotional state, cognitive load, and performance of junior residents in simulated resuscitation\u003c/em\u003e, (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHempel G, Struck HW, Piegeler MF, Rotzoll T (2019) D. \u003cem\u003eImpact of quantitative feedback via high-fidelity airway management training on success rate in endotracheal intubation in undergraduate medical students-A prospective single-center study\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill PP, Anderson DDA, Talbert M, Maraj S (2024) C. \u003cem\u003eRemembering to Resume: A Randomized Trial Comparing Combined Interruption Management Training and Simulation-Based Education to Simulation-Based Education Alone\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirsch AW, Nagler J (2020) \u003cem\u003eSpaced Education Through e-Learning for Ongoing Professional Development\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou M, Liu LY, Luo Y, Xue W, Yuan H, Zhang H (2024) X. \u003cem\u003eThe impact of virtual reality applied in nurse education on students' clinical thinking: A systematic review\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgnacio, J., Dolmans, Diana, Scherpbier, Albert, Rethans, Jan-Joost, Lopez, Violeta,Liaw, \u0026hellip; \u003cem\u003eDevelopment, implementation, and evaluation of a mental rehearsal strategy to improve clinical performance and reduce stress: A mixed methods study\u003c/em\u003e, (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwama Y, Ikeda M (2024) \u003cem\u003eExploring a metacognitive approach for case analysis based learning of anxiety adjustment in nurses: a qualitative study\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJannin L, Ganier F, De Vries P (2019) \u003cem\u003eAtomized or Delayed Execution? An Alternative Paradigm for the Study of Procedural Learning\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemp S et al (2019) Medical student wellbeing - a consensus statement from Australia and New Zealand. BMC Med Educ 19:69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein M, Otto, Barbel, Fischer MR, Stark (2019) Robin. \u003cem\u003eFostering medical students' clinical reasoning by learning from errors in clinical case vignettes: effects and conditions of additional prompting procedures to foster self-explanations\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Rochelle JS, Pangaro DSJ, Artino LN, van der Vleuten AR, Schuwirth CP (2011) L. \u003cem\u003eAuthenticity of instruction and student performance: a prospective randomised trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J, Donkers J, Jarodzka H, Sellenraad G, Faber TJE, van Merri\u0026euml;nboer JJG (2024) \u003cem\u003eThe Effects of Reflective Pauses on Performance in Simulation Training\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin TK, Werner K, Kak M, Herbst CH (2023) Health-care worker retention in post-conflict settings: a systematic literature review. Health Policy Plan 38:109\u0026ndash;121\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, A., Duffy, Melissa, Tse, Sandy, Zucker, Marc, McMillan, Hugh, Weldon, Patrick,Quet, Julie\u0026hellip; \u003cem\u003eConcurrent versus terminal feedback: The effect of feedback delivery on lumbar puncture skills in simulation training\u003c/em\u003e, (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Rippe J., R.M., Velez-Florez M.C., Amiruddin R., Lerebo W., Gokli A., Francavilla M., R\u0026hellip; \u003cem\u003eRADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load\u003c/em\u003e, (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaia D, Andrade R, Afonso J, Costa P, Valente C, Espregueira-Mendes J (2023) \u003cem\u003eAcademic Performance and Perceptions of Undergraduate Medical Students in Case-Based Learning Compared to Other Teaching Strategies: A Systematic Review with Meta-Analysis\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManasia AR et al (2005) Feasibility and potential clinical utility of goal-directed transthoracic echocardiography performed by noncardiologist intensivists using a small hand-carried device (SonoHeart) in critically ill patients. J Cardiothorac Vasc Anesth 19:155\u0026ndash;159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManning KD, Spicer JO (2021) Golub, Lucas, Akbashev, Mikhail, Klein, Robin. \u003cem\u003eThe micro revolution: effect of Bite-Sized Teaching (BST) on learner engagement and learning in postgraduate medical education\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarei HF, Donkers J, Al-Eraky MM, Van Merrienboer JJG (2019) Collaborative use virtual patients after lecture enhances Learn minimal Invest Cogn load\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMargallo, M.C.M., Mariano, Carmela Nicole P., Martinez, Kryssia Nadine Fauve L.,Maria\u0026hellip; \u003cem\u003eCharacterizing effective teaching in nursing pharmacology YouTube videos: A mixed methods study\u003c/em\u003e, (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcConnaughey S, Freeman, Rosario, Kim S, Sheehan F (2018) \u003cem\u003eIntegrating Scaffolding and Deliberate Practice Into Focused Cardiac Ultrasound Training: A Simulator Curriculum\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormick E, Devine S, Crilly J, Brough P, Greenslade J (2023) Measuring occupational stress in emergency departments. Emerg Med Australas 35:234\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eModi H, Singh H, Orihuela-Espina F, Athanasiou T, Fiorentino F, Yang GZ, Darzi A, Leff DR (2018) Temporal Stress Operating Room: Brain Engagem Promotes Coping Disengagement Prompts Choking\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrill C, Hardin J, Steiman J (2023) Perioperative Reflection: An Understudied Yet Common Practice Among Surgical Residents. J Surg Educ 80:428\u0026ndash;433\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarayanan A, Kong NM, Pearson V, Mani L, Fisher K, Khashram JP (2024) M. \u003cem\u003eBroad Responses and Attitudes to Having Music in Surgery (The BRAHMS Study): An Australia and Aotearoa New Zealand Perspective\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNatesan S, Bailitz J, King A, Krzyzaniak SM, Kennedy SK, Kim AJ, Byyny R, Gottlieb M (2020) \u003cem\u003eClinical Teaching: An Evidence-based Guide to Best Practices from the Council of Emergency Medicine Residency Directors\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicholls D, Sweet L, Hyett J, Muller A (2020) Push and pull factors impacting the pedagogical approaches used by sonographers to teach scanning skills. Australas J Ultrasound Med 23:220\u0026ndash;226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoble VE et al (2009) Evaluation of a thoracic ultrasound training module for the detection of pneumothorax and pulmonary edema by prehospital physician care providers. BMC Med Educ 9:3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOade S, Storey SK, Timme J (2019) C. \u003cem\u003eWORK TOGETHER, LEARN TOGETHER: THE BENEFITS OF INITIATING IN-SITU SIMULATION IN AN ACUTE MEDICINE DEPARTMENT\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandit A, de Gouveia M, Horsfall HL, Reka A, Marcus HJ (2022) \u003cem\u003eEfficacy of a Mindfulness-Based Intervention in Ameliorating Inattentional Blindness Amongst Young Neurosurgeons: A Prospective, Controlled Pilot Study\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePassalacqua C, Malhan LM (2023) N. Reducing Cogn Overload Resusc Room\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePusic MV et al (2012) Experience curves as an organizing framework for deliberate practice in emergency medicine learning. Acad Emerg Med 19:1476\u0026ndash;1480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao Bhagavathula V, Bhagavathula, Viswakanth M, Chaudhuri RS, Joydeep Dutta (2022). \u003cem\u003eThe Integration of Prelaboratory Assignments within Neuroanatomy Augment Academic Performance, Increase Engagement, and Enhance Intrinsic Motivation in Students\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson KM, Fouquet SD, Kerns, Ellen MC, Russell J (2019) \u003cem\u003eImpact of Mobile Device-Based Clinical Decision Support Tool on Guideline Adherence and Mental Workload\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuiz JG, Levinson CD (2009) AJ. \u003cem\u003eComputer animations in medical education: a critical literature review\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanderson, B.J., Field, Jeremy D, Kocaballi, Ahmet B, Estcourt, Lise J, Magrabi,Farah, Wood\u0026hellip; \u003cem\u003eClinical decision support versus a paper-based protocol for massive transfusion: Impact on decision outcomes in a simulation study\u003c/em\u003e, (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchick, K., Reiser, S, Janssen, L, Schacht, L, Pittroff, SID, Dorfler, E, Klein,E, Roenneberg, C,\u0026hellip; \u003cem\u003eTraining in medical communication competence through video-based e-learning: How effective are video modeling and video reflection?\u003c/em\u003e (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlorhaufer C, Behrends M, Diekhaus G, Keberle M, Weidemann J (2012) \u003cem\u003eImplementation of a web-based, interactive polytrauma tutorial in computed tomography for radiology residents: how we do it\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmitz F, Schnabel KP, Stricker D, Fischer MR, Guttormsen (2017) S. \u003cem\u003eLearning communication from erroneous video-based examples: A double-blind randomised controlled trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSewell JL, Cate BJ, O'Sullivan OT, Shah P, Boscardin BJ (2019) C. \u003cem\u003eEXPERIENCED ENDOSCOPY TEACHERS USE TEACHING STRATEGIES THAT ADDRESS LEARNER CHALLENGES AND ALIGN WITH COGNITIVE LOAD THEORY\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSewell JL, Boscardin YJQ, Cate C, O'Sullivan OT (2019) P. \u003cem\u003eIMPACT OF SPECIFIC TEACHING STRATEGIES ON FELLOW COGNITIVE LOAD DURING COLONOSCOPY TRAINING\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSim, J.J.M., Rusli, Khairul Dzakirin Bin, Seah, Betsy, Levett-Jones, Tracy, Lau,Ying, Li\u0026hellip; \u003cem\u003eVirtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis\u003c/em\u003e, (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiller K, Ossenberg C, Takashima M, Henderson A (2025) \u003cem\u003eComparable achievement of workplace performance of student and graduate nurses: A quantitative cohort evaluation\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStark R, Kopp V, Fischer MR (2009) \u003cem\u003eFacilitating Medical Students' Diagnostic Competence Via Situated, Case-Based Learning With Worked-Out Examples: The Influence of Errors and Feedback\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStyn A, Scheiter K, Fischer MR, Shiozawa T, Behrmann F, Steffan A, Kugelmann D, Berndt M (2023) \u003cem\u003eEffects of tablet-based drawing and paper-based methods on medical students' learning of gross anatomy\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaveira-Gomes T, Prado-Costa R, Severo M, Ferreira MA (2015) \u003cem\u003eCharacterization of medical students recall of factual knowledge using learning objects and repeated testing in a novel e-learning system\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTremblay M-L, Leppink, Jimmie, Leclerc G, Rethans J-J, Dolmans, Diana HJM (2019) \u003cem\u003eSimulation-based education for novices: complex learning tasks promote reflective practice\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUraiby H, Gordon G-CC, Sereno M, Powell M, McCarthy B (2021) M. \u003cem\u003eFostering intrinsic motivation in remote undergraduate histopathology education\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUslu-Sahan F, Ozdemir, Leyla K, Mert M, Yildirim, Sevda, Odabasi O (2025) \u003cem\u003eVirtual reality and standardized patient simulation programs in chemotherapy education for breast cancer: A comparative analysis of nursing students' knowledge, cognitive load, satisfaction and confidence\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVignon P et al (2007) Focused training for goal-oriented hand-held echocardiography performed by noncardiologist residents in the intensive care unit. Intensive Care Med 33:1795\u0026ndash;1799\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVydro SA, Drapeau LC, Perez S, Romero A, Norris D, Harmon M, Sewell D (2023) J. Implementation of a 3D-Printed Color-Coded Tracheobronchial Tree for Bronchoscopy Training and Anatomy Learning in a Pulmonary. Crit Care Fellowsh Program\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWahyuni S, Cahyati B, Limakrisna P (2020) N. \u003cem\u003eThe Influence of Self Efficacy on Cognitive Load In Online Learning In The Pandemic Covid-19 On Nursing Students\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang T, Zheng J, Tan CY, Lajoie SP (2023) \u003cem\u003eComputer-based scaffoldings influence students' metacognitive monitoring and problem-solving efficiency in an intelligent tutoring system\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerner A, Holderried F, Schaffeler N, Weyrich P, Riessen R, Zipfel S, Celebi N (2013) \u003cem\u003eCommunication training for advanced medical students improves information recall of medical laypersons in simulated informed consent talks - a randomized controlled trial\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf J, Lohmeyer LD, Farshad Q, Furnstahl M, Meboldt P (2023) M. \u003cem\u003eHow different augmented reality visualizations for drilling affect trajectory deviation, visual attention, and user experience\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo C, Ungi MA, Lasso T, Jalink A, Fichtinger D, Nanji G (2017) S. \u003cem\u003e3D segmentation as a surgical planning tool for residents in liver resection surgery\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo, C.T., MacDonald, Andrew, Ungi, Tamas, Lasso, Andras, Jalink, Diederick, Zevin,Boris, F\u0026hellip; \u003cem\u003eUtility of 3D Reconstruction of 2D Liver Computed Tomography/Magnetic Resonance Images as a Surgical Planning Tool for Residents in Liver Resection Surgery\u003c/em\u003e, (2018)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeo L, Romero R (2022) Optical ultrasound simulation-based training in obstetric sonography. J Matern Fetal Neonatal Med 35:2469\u0026ndash;2484\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYiin S-J, Chern C-L (2023) \u003cem\u003eThe effects of an active learning mechanism on cognitive load and learning achievement: A new approach for pharmacology teaching to Taiwanese nursing students\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNamara T et al (2025) Short and Sweet: Reducing Extrinsic Cognitive Load When Authoring Self-Study Materials for Medical Students. Med Sci Educ\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson AS et al (2025) Assessment of Emergency Medicine Residents' Situational Awareness and Perception of Patient Safety Culture in the Emergency Department. AEM Educ Train 9:e70060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson KD et al (2025) Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Netw Open 8:e2534976\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudson TJ et al (2025) Impact of Ambient Artificial Intelligence Documentation on Cognitive Load. Mayo Clin Proc Digit Health 3:100193\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsyrulnik A et al (2026) When SMART Isn't Always Best: An Investigation of Emergency Medicine Resident Goal Setting Practices. AEM Educ Train 10:e70125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshman G, Kalyuga S, Sweller J (2020) Problem-solving or Explicit Instruction: Which Should Go First When Element Interactivity Is High? Educ Psychol Rev 32:229\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaivas M, Lambert MJ, Harwood RA, Wood JP, Konicki J (2000) Lower-extremity Doppler for deep venous thrombosis\u0026ndash;can emergency physicians be accurate and fast? Acad Emerg Med 7:120\u0026ndash;126\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleur DS, Bredeweg B (2021) Bos, W. Metacognition: ideas and insights from neuro- and educational sciences. NPJ Sci Learn 6:13van den\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePintrich PR (2002) The Role of Metacognitive Knowledge in Learning, Teaching, and Assessing. Theory Into Pract 41:219\u0026ndash;225\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman BJ (1990) Self-Regulated Learning and Academic Achievement: An Overview. Educational Psychol 25:3\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah J, Vyas A, Vyas D (2014) The History of Robotics in Surgical Specialties. Am J Robot Surg 1:12\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostello Anthony (2023) Junior surgeons must be trained for robotics revolution. MJA Insights\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleming CA et al (2022) Surgical trainee experience and opinion of robotic surgery in surgical training and vision for the future: a snapshot study of pan-specialty surgical trainees. J Robot Surg 16:1073\u0026ndash;1082\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBostrom MPG, Lewis KO, Berger G (2021) Orthopaedic Electronic Learning Graduate Medical Education Consensus Working, G. The Role of Electronic Learning in Orthopaedic Graduate Medical Training: A Consensus From Leaders in Orthopaedic Training Programs. J Am Acad Orthop Surg 29:317\u0026ndash;325\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePell G, Fuller R, Homer M, Roberts T (2012) Is short-term remediation after OSCE failure sustained? A retrospective analysis of the longitudinal attainment of underperforming students in OSCE assessments. Med Teach 34:146\u0026ndash;150\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMills G, Ruzycki SM, Sabourin J, Dance E (2021) Experiences of breastfeeding among women residents in Alberta: a cross-sectional survey. Postgrad Med 133:42\u0026ndash;47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStentz NC, Griffith KA, Perkins E, Jones RD, Jagsi R (2016) Fertility and Childbearing Among American Female Physicians. J Womens Health (Larchmt) 25:1059\u0026ndash;1065\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRangel EL et al (2021) Incidence of Infertility and Pregnancy Complications in US Female Surgeons. JAMA Surg 156:905\u0026ndash;915\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashemi SB, Tan F, Kenneth; and, Bain CA (2019) Addressing Cognitive Load in Training on Electronic Medical Record Systems. \u003cem\u003eACIS 2019 Proceedings\u003c/em\u003e 56\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinghal K et al (2023) Large language models encode clinical knowledge. Nature\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAruta J, Almazan JU, Alamri MS, Adolfo CS, Gonzales F (2022) Measuring mental well-being among frontline nurses during the COVID-19 crisis: Evidence from Saudi Arabia. Curr Psychol, 1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSewell JL, Boscardin CK, Young JQ, Ten Cate O (2016) O'Sullivan, P.S. Measuring cognitive load during procedural skills training with colonoscopy as an exemplar. Med Educ 50:682\u0026ndash;692\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal A (2024) University of Bergen, Norway\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBansal A et al (2024) Role of Ultrasound-Based Therapies in Cardiovascular Diseases. Struct Heart, 100349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson RC, Shiffrin RM, Human Memory (1968) A Proposed System and its Control Processes. in \u003cem\u003ePsychology of Learning and Motivation\u003c/em\u003e, Vol. 2 (eds. Spence, K.W. \u0026amp; Spence, J.T.) 89\u0026ndash;195Academic Press\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Bergen","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive load, healthcare workers, patient safety, burnout, operative performance, well-being, simulation, deliberate practice, metacognition","lastPublishedDoi":"10.21203/rs.3.rs-9108201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9108201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Cognitive load is the mental effort required to perform a task due to demands on working memory storage and information processing. In clinical training, the cognitive load can be high due to the task complexity. Applying cognitive load management strategies may help educators support trainees’ well-being, retention, and performance. Limited research exists on the translating cognitive load management strategies with practical recommendations for enhancing clinical trainees' retention, well-being, and performance.\u003c/p\u003e\n\u003cp\u003eObjectives: This scoping review aims to identify cognitive load factors and management strategies that can be used to improve trainees’ well-being, retention, and performance in clinical training and practice.\u003c/p\u003e\n\u003cp\u003eMethods: We investigated the impacts of different cognitive load factors and management strategies on trainees’ well-being, retention, and performance, through a scoping review with narrative synthesis. No meta-analysis was planned. We followed PRISMA-ScR for search and screening. Databases (Embase, MEDLINE, Web of Science, CINAHL) searched 18-19 February 2025; Google Scholar and hand‑searches updated 27 February 2026.\u003c/p\u003e\n\u003cp\u003eResults: We identified 8,395 records (Embase 110; Medline 124; Web of Science 188; CINAHL 66; Google Scholar and hand searches 7,907). After deduplication, screening and full-text review, 125 studies were included. Cognitive load management strategies were clustered here as (i) optimising intrinsic load via task segmentation and part-task/step-up sequencing; (ii) minimising extraneous load via de-cluttering, supportive environments and clear and concise instructions; (iii) optimising germane load via structured feedback, worked examples, and appropriately staged simulation.\u003c/p\u003e\n\u003cp\u003eConclusions: Cognitive load management strategies and institutional support may be important for trainee retention, well-being, and performance in clinical training. By using evidence-based strategies, healthcare systems may help trainees better manage cognitive load and improve learning and performance.\u003c/p\u003e","manuscriptTitle":"Cognitive load in clinical training: a scoping review of factors and strategies linked to well‑being, retention, and performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 10:13:28","doi":"10.21203/rs.3.rs-9108201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"927362de-0be6-4afa-981d-310ea2cae82a","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64419933,"name":"Educational Psychology"},{"id":64419934,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2026-03-16T10:13:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 10:13:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9108201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9108201","identity":"rs-9108201","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}