Comparing the Effectiveness of Multimodal Learning Using Computer-Based and Immersive Virtual Reality Simulation–Based Interprofessional Education With Co-Debriefing, Medical Movies, and Massive Online Open Courses for Mitigating Stress and Long-Term Burnout in Medical Training: Quasi-Experimental Study
Notice bibliographique
Résumé
BACKGROUND: Burnout among emergency room health care workers (HCWs) has reached critical levels, affecting up to 43% of HCWs and 35% of emergency medicine personnel during the COVID-19 pandemic. Nurses were most affected, followed by physicians, leading to absenteeism, reduced care quality, and turnover rates as high as 78% in some settings such as Thailand. Beyond workforce instability, burnout compromises patient safety. Each 1-unit increase in emotional exhaustion has been linked to a 2.63-fold rise in reports of poor care quality, 30% increase in patient falls, 47% increase in medication errors, and 32% increase in health care-associated infections. Burnout is also associated with lower job satisfaction, worsening mental health, and increased intent to leave the profession. These findings underscore the urgent need for effective strategies to reduce stress and burnout in emergency care. OBJECTIVE: This study aimed to evaluate the effectiveness and effect size of a multimodal learning approach-Emergency Room Virtual Simulation Interprofessional Education (ER-VIPE)-that integrates medical movies, massive online open courses (MOOCs), and computer- or virtual reality (VR)-based simulations with co-debriefing for reducing burnout and stress among future health care professionals compared with approaches lacking co-debriefing or using only movies and MOOCs. METHODS: A single-blind, quasi-experimental study was conducted at a university hospital from August 2022 to September 2023 using a 3-group treatment design. Group A (control) participated in a 3D computer-based, simulation-based interprofessional education (SIMBIE) without debriefing. Group B received the ER-VIPE intervention. Group C received the same as Group B, but the computer-based SIMBIE was replaced with 3D VR-SIMBIE. SIMBIE activities simulated a COVID-19 pneumonia crisis. Outcomes included the Dundee Stress State Questionnaire (DSSQ) and the Copenhagen Burnout Inventory, with trait anxiety as a behavioral control. Stress and burnout were measured at baseline, pre-intervention, postintervention, and 1-month follow-up. Generalized estimating equations were used to analyze group differences, with statistical significance set at P<.05. RESULTS: We randomized 87 undergraduate students from various health programs into the 3 groups (n=29 each). Participants' mean age was 22 years, with 71% (62/87) as women. After the 1-month post-SIMBIE follow-up, adjusted analyses revealed positive trends in DSSQ-engagement across all groups, with Group B showing a significant increase compared with Group A (mean difference=3.93; P=.001). DSSQ-worry and DSSQ-distress scores decreased nonsignificantly across all groups. Burnout scores also improved across groups, with Group B having a significantly lower score than Group A (mean difference=-2.02; P=.02). No significant burnout differences were found between Group C and Groups A or B. CONCLUSIONS: A multimodal learning approach combining medical movies, MOOCs, and 3D computer-based SIMBIE with co-debriefing effectively improved engagement, reduced stress, and lowered burnout among future health care professionals. This scalable educational framework may help enhance well-being and resilience in high-pressure clinical environments.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».