Multimodal Cluster Analysis of Medical Residents' Emotions During High‐Fidelity Harassment Bystander Simulation.
Background: High fidelity simulations can be an effective tool for anti‐harassment education. While emotions have been identified as crucial in simulation‐based education, their role in anti‐harassment education within medical training remains underexplored. Objectives: We aimed to investigate emoti...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 5; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188234198&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188234198 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Oct2025 vid: 41 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 188234198 188234198 188234198 10.1111/jcal.70099 188234198 ppf: 1 ppct: 18 formats: tig: atl: Multimodal Cluster Analysis of Medical Residents' Emotions During High‐Fidelity Harassment Bystander Simulation. aug: au: Ahn, Byunghoon Matin, Negar Johnson, Myriam Lee, So Yeon Sun, Ning‐Zi Harley, Jason M. affil: Department of Surgical and Interventional Sciences, McGill University, Montreal, Canada sug: subj: Interns and Residents Psychosocial Factors Emotions Simulations Internal Medicine Bullying Education Outcomes of Education Education, Medical Human Cluster Analysis Self Report Wearable Sensors Content Analysis Post Hoc Analysis North America Videorecording One-Way Analysis of Variance Fisher's Exact Test Analysis of Variance Descriptive Statistics Male Female Adult Purposive Sample Convenience Sample Data Analysis Software Funding Source Adult: 19-44 years Male Female ab: Background: High fidelity simulations can be an effective tool for anti‐harassment education. While emotions have been identified as crucial in simulation‐based education, their role in anti‐harassment education within medical training remains underexplored. Objectives: We aimed to investigate emotional profiles of medical residents during harassment bystander simulation training via hierarchical clustering based on multimodal emotions data. Methods: Twenty seven internal medicine residents with complete data sets that were part of a larger study were recruited. Emotions were captured through self‐report surveys, an electronic bracelet that records electrodermal activity, and speech content analysis based on the residents' simulation debriefing. The study involved residents performing a simulated central line insertion while a simulated harassment took place that they could use to practice intervening in harassment. Results: Our cluster analysis revealed three equal‐sized groups: 'Emotionally Balanced, Minimal Arousal', 'Positive, Spiked Arousal' and 'Negative High Arousal'. The clusters had distinct levels of self‐report emotions and electrodermal activity. Content analysis revealed distinct emotions, and sources of emotions between the clusters. Post hoc analysis revealed that the 'Emotionally Balanced, Minimal Arousal' group showed a higher propensity for directly confronting the harasser, indicating a composed emotional state conducive to focusing on simulation objectives. Conclusions: Our findings reveal the varied emotional profiles that can be expected in simulation‐based medical education and underscore the value of a multimodal approach to understanding these dynamics. Furthermore, the study highlights the criticality of recognising the sources of emotions and promoting effective emotion regulation strategies, especially in authentic learning environments where emotional responses are complex and impactful. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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