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Enregistrement W7165567896 · doi:10.17863/cam.131424

Developing and Evaluating Learning Theory-Informed Extended Reality Simulations in Medical Education: Lessons from a Pilot Randomised Controlled Trial

2025· dissertation· en· W7165567896 sur OpenAlexaboutno aff
Ruby Woodward

Notice bibliographique

RevueApollo (University of Cambridge) · 2025
Typedissertation
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedical simulationPsychological interventionRandomized controlled trialIntervention (counseling)Simulation trainingSimulated patient

Résumé

récupéré en direct d'OpenAlex

Introduction: Despite hopes that the adoption of extended reality (XR) technology in medical simulation could offer a scalable and accessible teaching option in medical education, the development of novel XR-enhanced simulation training interventions is rarely underpinned by a learning theoretical framework. Additionally, there is a notable lack of robust experimental studies evaluating the effectiveness of XR-enhanced simulation in comparison to more established simulation modalities, with existing and validated assessment instruments seldom used to assess outcomes. Addressing these gaps, I worked with an interdisciplinary team (University of Cambridge, Cambridge University Hospitals, and GigXR) to develop and evaluate ‘Holoscenarios,’ a mixed reality (MR) enhanced simulation training intervention grounded in constructivism, designed for medical students to practice the assessment and management of acute medical scenarios. I then designed and implemented a pilot randomised controlled trial (RCT) to evaluate the effectiveness of Holoscenarios versus manikin-based simulation (MBS). The primary objective of this study was to assess the processes, resources, and management strategies required for running an experimental study comparing the effectiveness of an XR-enhanced simulation intervention with an established simulation training modality. Additionally, I aimed to gather preliminary data on whether Holoscenarios was non-inferior to MBS in providing medical students with the technical (TS) and non-technical skills (NTS) required to manage an acutely deteriorating patient. Methodology: The completed module of Holoscenarios comprised three 20-minute interactive acute medical scenarios surrounding the assessment and management of an acutely deteriorating patient. Scenarios were depicted using holographic overlays viewed through the HoloLens 2, and aligned with learning outcomes based on the General Medical Council’s (GMC) Outcomes for Graduates. A pilot RCT was integrated into final-year medical students' simulation training days, utilising a pre-post-test control group design. Over seven training days, with six students attending each day, participants were randomly allocated to an MBS or MR simulation training course. Participants in each course completed three facilitated simulated acute medical scenarios matched in content. Participants' technical skills (TS) and non-technical skills (NTS) surrounding the assessment and management of an acutely deteriorating patient were evaluated at baseline and immediately after training courses via a 10-minute pre-test and post-test, each comprising an observed simulated acute medical scenario. All pre- and post-tests were assessed using pre-existing standardised assessment instruments: the Queens’ Simulation Assessment Tool (QSAT), a modifiable anchored rating scale designed for the competency-based assessment of simulated resuscitation scenarios, and the Ottawa Global Rating Scale (GRS), a behavioural rating scale designed to assess six categories of NTS in simulated emergency scenarios, both of which aligned with the learning outcome of Holoscenarios. To measure students’ engagement and experience following training, the Satisfaction with Simulation Experience Scale (SSE) was employed. Additionally, self-reported confidence was evaluated at baseline and post-training utilising a newly developed Likert scale that rated participants' self-reported confidence in performing skills related to assessing and managing an acutely deteriorating patient. The scores from these assessment rubrics were then compared from pre-test to post-test within groups utilising a Wilcoxon Signed-Rank Test, with changes in scores from pre-training to post-training compared between the MR and MBS groups using a Mann-Whitney U test. Results: Data collection was completed in May 2024, with 28 participants completing the pilot RCT. Preliminary findings showed that both MR and MBS groups showed a significant improvement in TS and NTS across all assessment domains from pre-training to post-training. Notably, there were no significant differences in the improvements between the two theoretically based interventions, suggesting that MR is comparable to traditional MBS training methods in improving the TS and NTS required to assess and manage an acutely deteriorating patient. Moreover, students in both groups reported increased self-reported confidence in applying the skills required to assess and manage acutely deteriorating patients. While the educational outcomes were promising, I encountered significant operational challenges whilst implementing this RCT. This study necessitated integration into final-year medical students’ existing training days to mitigate simulation centre costs. Data collection periods were therefore limited to students’ existing rotas, restricting the number of participants available to enrol in the study. Both simulation training courses required skilled staff for setup and operation, with MR courses requiring additional technical support. Maintaining high standards for both MBS and MR courses necessitated three simulation technicians, who underwent 2 days of training before the simulation courses. Three clinical facilitators per simulation day were required, relying on clinical staff to take time away from clinical commitments voluntarily. Similarly, maintaining standardisation of the intervention and assessment between groups was compromised due to differences in facilitation, group composition and variability between assessors. Discussion: This project sought to address the existing gap in theoretical frameworks underpinning the development of innovative XR-enhanced simulation training interventions. Grounded in constructivist learning theory, Holoscenarios emphasises a strategy for leveraging MR technology to create immersive and realistic scenarios that promote problem-solving and reflection on decision-making. Furthermore, this study provides preliminary evidence suggesting that MR-enhanced simulation is at least comparable to MBS in improving the TS, NTS, and self-reported confidence related to the assessment and management of acutely deteriorating patients among undergraduate medical students. These improvements align with the constructivist principles underpinning the design of Holoscenarios, which suggests that an interactive and immersive MR experience enables students to engage meaningfully with clinical problems to build upon existing knowledge and develop new skills. Furthermore, this research addresses the lack of robust experimental research supporting the use of XR-enhanced simulation in medical education. Initial findings contribute to the existing literature by highlighting the unique operational and logistical challenges of running experimental research in this domain, which are commonly encountered in medical education and simulation research. These challenges were analysed to formulate future recommendations for mitigating these challenges whilst identifying the resources required to do so, and acknowledging issues that may be unsurmountable in this context. In doing so, this study lays a foundational framework for executing future large-scale experimental research evaluating an XR-enhanced simulation intervention. As the development of XR-enhanced teaching tools in medical education evolves, future research designs must account for the challenges and mitigation strategies outlined in this work.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,154
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,063
Tête enseignante GPT0,412
Écart entre enseignants0,349 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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