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Key feature-cases as virtual patients in education of veterinary neurology
To provide students of veterinary medicine with the necessary day 1 competences, e-learning offerings are increasingly used in addition to classical teaching formats such as lectures. For example, virtual patients offer the possibility of case-based, computer-assisted learning. A concept to teach an...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437545/ https://www.ncbi.nlm.nih.gov/pubmed/36061112 http://dx.doi.org/10.3389/fvets.2022.911026 |
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author | Reeh, Solveig Brigitta Kleinsorgen, Christin Schaper, Elisabeth Volk, Holger Andreas Tipold, Andrea |
author_facet | Reeh, Solveig Brigitta Kleinsorgen, Christin Schaper, Elisabeth Volk, Holger Andreas Tipold, Andrea |
author_sort | Reeh, Solveig Brigitta |
collection | PubMed |
description | To provide students of veterinary medicine with the necessary day 1 competences, e-learning offerings are increasingly used in addition to classical teaching formats such as lectures. For example, virtual patients offer the possibility of case-based, computer-assisted learning. A concept to teach and test clinical decision-making is the key feature (KF) approach. KF questions consist of three to five critical points that are crucial for the case resolution. In the current study usage, learning success, usability and acceptance of KF cases as neurological virtual patients should be determined in comparison to the long cases format. Elective courses were offered in winter term 2019/20 and summer term 2020 and a total of 38 virtual patients with neurological diseases were presented in the KF format. Eight cases were provided with a new clinical decision-making application (Clinical Reasoning Tool) and contrasted with eight other cases without the tool. In addition to the evaluation of the learning analytics (e.g., processing times, success rates), an evaluation took place after course completion. After 229 course participations (168 individual students and additional 61 with repeated participation), 199 evaluation sheets were completed. The average processing time of a long case was 53 min, while that of a KF case 17 min. 78% of the long cases and 73% of KF cases were successfully completed. The average processing time of cases with Clinical Reasoning Tool was 19 min. The success rate was 58.3 vs. 60.3% for cases without the tool. In the survey, the long cases received a ranking (1 = very good, 6 = poor) of 2.4, while KF cases received a grade of 1.6, 134 of the respondents confirmed that the casework made them feel better prepared to secure a diagnosis in a real patient. Flexibility in learning (n = 93) and practical relevance (n = 65) were the most frequently listed positive aspects. Since KF cases are short and highlight only the most important features of a patient, 30% (n = 70) of respondents expressed the desire for more specialist information. KF cases are suitable for presenting a wide range of diseases and for training students' clinical decision-making skills. The Clinical Reasoning Tool can be used for better structuring and visualizing the reasoning process. |
format | Online Article Text |
id | pubmed-9437545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94375452022-09-03 Key feature-cases as virtual patients in education of veterinary neurology Reeh, Solveig Brigitta Kleinsorgen, Christin Schaper, Elisabeth Volk, Holger Andreas Tipold, Andrea Front Vet Sci Veterinary Science To provide students of veterinary medicine with the necessary day 1 competences, e-learning offerings are increasingly used in addition to classical teaching formats such as lectures. For example, virtual patients offer the possibility of case-based, computer-assisted learning. A concept to teach and test clinical decision-making is the key feature (KF) approach. KF questions consist of three to five critical points that are crucial for the case resolution. In the current study usage, learning success, usability and acceptance of KF cases as neurological virtual patients should be determined in comparison to the long cases format. Elective courses were offered in winter term 2019/20 and summer term 2020 and a total of 38 virtual patients with neurological diseases were presented in the KF format. Eight cases were provided with a new clinical decision-making application (Clinical Reasoning Tool) and contrasted with eight other cases without the tool. In addition to the evaluation of the learning analytics (e.g., processing times, success rates), an evaluation took place after course completion. After 229 course participations (168 individual students and additional 61 with repeated participation), 199 evaluation sheets were completed. The average processing time of a long case was 53 min, while that of a KF case 17 min. 78% of the long cases and 73% of KF cases were successfully completed. The average processing time of cases with Clinical Reasoning Tool was 19 min. The success rate was 58.3 vs. 60.3% for cases without the tool. In the survey, the long cases received a ranking (1 = very good, 6 = poor) of 2.4, while KF cases received a grade of 1.6, 134 of the respondents confirmed that the casework made them feel better prepared to secure a diagnosis in a real patient. Flexibility in learning (n = 93) and practical relevance (n = 65) were the most frequently listed positive aspects. Since KF cases are short and highlight only the most important features of a patient, 30% (n = 70) of respondents expressed the desire for more specialist information. KF cases are suitable for presenting a wide range of diseases and for training students' clinical decision-making skills. The Clinical Reasoning Tool can be used for better structuring and visualizing the reasoning process. Frontiers Media S.A. 2022-08-19 /pmc/articles/PMC9437545/ /pubmed/36061112 http://dx.doi.org/10.3389/fvets.2022.911026 Text en Copyright © 2022 Reeh, Kleinsorgen, Schaper, Volk and Tipold. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Veterinary Science Reeh, Solveig Brigitta Kleinsorgen, Christin Schaper, Elisabeth Volk, Holger Andreas Tipold, Andrea Key feature-cases as virtual patients in education of veterinary neurology |
title | Key feature-cases as virtual patients in education of veterinary neurology |
title_full | Key feature-cases as virtual patients in education of veterinary neurology |
title_fullStr | Key feature-cases as virtual patients in education of veterinary neurology |
title_full_unstemmed | Key feature-cases as virtual patients in education of veterinary neurology |
title_short | Key feature-cases as virtual patients in education of veterinary neurology |
title_sort | key feature-cases as virtual patients in education of veterinary neurology |
topic | Veterinary Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437545/ https://www.ncbi.nlm.nih.gov/pubmed/36061112 http://dx.doi.org/10.3389/fvets.2022.911026 |
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