An Alternative to Traditional Bedside Teaching During COVID-19: High-Fidelity Simulation-Based Study
Bibliographic record
Abstract
BACKGROUND: Bedside teaching is integral to medical education and has been highlighted to improve clinical and communication skills, as well as clinical reasoning. Despite the significant advantages of bedside teaching, its usage within medical education has been declining, and COVID-19 has added additional challenges. The pandemic has resulted in a significant reduction in opportunities to deliver bedside teaching due to risk of viral exposure, patients declining student interactions, and ward closures. Educators have therefore been required to be innovative in their teaching methods, leading to the use of online learning, social media platforms, and simulation. Simulation-based education allows for learning in a low-risk environment and affords the opportunity for deliberated repeated practice with case standardization. The results demonstrate that simulation-based training can increase students' confidence, increase the rates of correct clinical diagnoses, and improve retention of skills and knowledge when compared with traditional teaching methods. OBJECTIVE: To mitigate the impact of COVID-19 upon bedside teaching for third year students at Hull York Medical School amid closure of the cardiorespiratory wards, a high-fidelity simulation-based model of traditional bedside teaching was designed and implemented. The objectives of the teaching session were to enable students to perform history taking and a focused cardiorespiratory clinical examination in a COVID-19-safe environment using SimMan 3G. METHODS: Four clinical teaching fellows with experience of simulation-based medical education scripted histories for 2 common cardiorespiratory cases, which were asthma and aortic stenosis. The simulation sessions were designed for students to take a focused cardiorespiratory history and clinical examination using SimMan 3G. All cases involved dynamic vital signs, and the simulator allowed for auscultation of an ejection systolic murmur and wheezing in accordance with the cases chosen. Key aspects of the pathologies, including epidemiology, differential diagnoses, investigations, and management, were summarized using an interactive PowerPoint presentation, followed by a debriefing session. RESULTS: In total, 12 third year medical students undertook the sessions, and overall feedback was highly positive. Of the 10 students who completed the feedback questionnaires, 90% (n=9) felt more confident in their clinical examination skills following the teaching; 100% (n=10) of the students responded that they would recommend the session to a colleague; and implementation of regular simulation was frequently requested on feedback. These results are in keeping with the current literature. CONCLUSIONS: Bedside teaching continues to face ongoing challenges from the COVID-19 pandemic as well as declining patient recruitment and fluctuations in clinical findings. The support for simulation-based medical education is derived from high-quality studies; however, studies describing the use of this technology for bedside teaching in the undergraduate curriculum are limited. The authors describe a highly effective teaching session amid the pandemic, which allowed for maintenance of staff and student safety alongside continued education during a challenging time for educators globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".