The association between quality of connections and diagnostic accuracy in student-generated concept maps for clinical reasoning education with virtual patients.
GMS J Med Educ
; 40(5): Doc61, 2023.
Article
em En
| MEDLINE
| ID: mdl-37881522
ABSTRACT
Objectives:
Concept maps are a learning tool that fosters clinical reasoning skills in healthcare education. They can be developed by students in combination with virtual patients to create a visual representation of the clinical reasoning process while solving a case. However, in order to optimize feedback, there is a need to better understand the role of connections between concepts in student-generated maps. Therefore, in this study we investigated whether the quality of these connections is indicative of diagnostic accuracy.Methods:
We analyzed 40 concept maps created by fifth-year medical students in the context of four virtual patients with commonly encountered diagnoses. Half of the maps were created by students who made a correct diagnosis on the first attempt; the other half were created by students who made an error in their first diagnosis. The connections in the maps were rated by two reviewers using a relational scoring system. Analysis of covariance was employed to examine the difference in mean connection scores among groups while controlling for the number of connections.Results:
There were no differences between the groups in the number of concepts or connections in the maps; however, maps made by students who made a correct first diagnosis had higher scores for the quality of connections than those created by students who made an incorrect first diagnosis (12.13 vs 9.09; p=0.03). We also observed students' general reluctance to use connections in their concept maps.Conclusion:
Our results suggest that the quality, not the quantity, of connections in concept maps is indicative of their diagnostic accuracy.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Estudantes de Medicina
/
Educação de Graduação em Medicina
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article