Analyzing multimodal data to understand medical trainees' regulation strategies and physiological responses in high- fidelity medical simulation scenarios.
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| Title: | Analyzing multimodal data to understand medical trainees' regulation strategies and physiological responses in high- fidelity medical simulation scenarios. |
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| Authors: | Moreno, Matthew1 (AUTHOR), Melo, Lucia Patino1 (AUTHOR), Grewal, Keerat1 (AUTHOR), Matin, Negar1 (AUTHOR), Azher, Sayed1 (AUTHOR), Harley, Jason M.1,2,3,4 (AUTHOR) jason.harley@mcgill.ca |
| Source: | Metacognition & Learning. Dec2024, Vol. 19 Issue 3, p1161-1213. 53p. |
| Subject Terms: | *Self-regulated learning, *Residents (Medicine), *Medical education, Medical simulation, Medical laws |
| Abstract: | Medical simulations allow trainees to work within teams to develop their self-regulated learning (SRL) and socially-shared regulated learning (SSRL) skills (Bransen et al., 2022). Both skillsets help to better prepare medical trainees for the multifaceted challenges inherent in clinical practice. SRL skills are imperative in empowering learners to optimize their performance and become autonomous guiders of their own learning (Jarvela & Hadwin, 2013), while SSRL skills are needed to ensure that teams can work collectively to regulate their behaviors and to regulate their own learning to make decisions (Hadwin & Oshige, 2011). Questions remain about not only how medical trainees' behaviors, regulation strategies, and physiological responses vary while they participate in a high-fidelity medical simulation, but how additional data channels to measure human response can provide indicators of teams' regulation strategies. Using a mixed-methods convergence design incorporating multimodal data (Azevedo & Gašević, 2019), including behavioral, SRL and SSRL codes, and electrodermal activity, researchers studied twenty-nine (N = 29) 1st to 3rd year medical residents as they engaged in high-fidelity simulation scenarios. Results suggest that the mean-level of psychophysiological activation increase as simulations progress, in conjunction with an increase in team-regulated learning strategies to manage the effective provision of patient care from initial contact through to the delivery of critical procedures. These results provide valuable insights into the advancement of a team regulation-based framework within a high-fidelity medical simulation environment, leveraging multimodal data to reach an understanding of medical trainees' adoption of team-based approaches to team-regulation during simulation scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Metacognition & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 180499035 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analyzing multimodal data to understand medical trainees' regulation strategies and physiological responses in high- fidelity medical simulation scenarios. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Moreno%2C+Matthew%22">Moreno, Matthew</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Melo%2C+Lucia+Patino%22">Melo, Lucia Patino</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Grewal%2C+Keerat%22">Grewal, Keerat</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Matin%2C+Negar%22">Matin, Negar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Azher%2C+Sayed%22">Azher, Sayed</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harley%2C+Jason+M%2E%22">Harley, Jason M.</searchLink><relatesTo>1,2,3,4</relatesTo> (AUTHOR)<i> jason.harley@mcgill.ca</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Metacognition+%26+Learning%22">Metacognition & Learning</searchLink>. Dec2024, Vol. 19 Issue 3, p1161-1213. 53p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Self-regulated+learning%22">Self-regulated learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Residents+%28Medicine%29%22">Residents (Medicine)</searchLink><br />*<searchLink fieldCode="DE" term="%22Medical+education%22">Medical education</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+simulation%22">Medical simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+laws%22">Medical laws</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Medical simulations allow trainees to work within teams to develop their self-regulated learning (SRL) and socially-shared regulated learning (SSRL) skills (Bransen et al., 2022). Both skillsets help to better prepare medical trainees for the multifaceted challenges inherent in clinical practice. SRL skills are imperative in empowering learners to optimize their performance and become autonomous guiders of their own learning (Jarvela & Hadwin, 2013), while SSRL skills are needed to ensure that teams can work collectively to regulate their behaviors and to regulate their own learning to make decisions (Hadwin & Oshige, 2011). Questions remain about not only how medical trainees' behaviors, regulation strategies, and physiological responses vary while they participate in a high-fidelity medical simulation, but how additional data channels to measure human response can provide indicators of teams' regulation strategies. Using a mixed-methods convergence design incorporating multimodal data (Azevedo & Gašević, 2019), including behavioral, SRL and SSRL codes, and electrodermal activity, researchers studied twenty-nine (N = 29) 1st to 3rd year medical residents as they engaged in high-fidelity simulation scenarios. Results suggest that the mean-level of psychophysiological activation increase as simulations progress, in conjunction with an increase in team-regulated learning strategies to manage the effective provision of patient care from initial contact through to the delivery of critical procedures. These results provide valuable insights into the advancement of a team regulation-based framework within a high-fidelity medical simulation environment, leveraging multimodal data to reach an understanding of medical trainees' adoption of team-based approaches to team-regulation during simulation scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Metacognition & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11409-024-09403-z Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 53 StartPage: 1161 Subjects: – SubjectFull: Self-regulated learning Type: general – SubjectFull: Residents (Medicine) Type: general – SubjectFull: Medical education Type: general – SubjectFull: Medical simulation Type: general – SubjectFull: Medical laws Type: general Titles: – TitleFull: Analyzing multimodal data to understand medical trainees' regulation strategies and physiological responses in high- fidelity medical simulation scenarios. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Moreno, Matthew – PersonEntity: Name: NameFull: Melo, Lucia Patino – PersonEntity: Name: NameFull: Grewal, Keerat – PersonEntity: Name: NameFull: Matin, Negar – PersonEntity: Name: NameFull: Azher, Sayed – PersonEntity: Name: NameFull: Harley, Jason M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 15561623 Numbering: – Type: volume Value: 19 – Type: issue Value: 3 Titles: – TitleFull: Metacognition & Learning Type: main |
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