Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring

Background Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the mentor-selection. We aimed to qualitatively characterize mentoring-profile-texts, identify differences in form and content and thus elements that promote s...

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Veröffentlicht in:BMC medical education Jg. 23; H. 1; S. 1 - 8
Hauptverfasser: Gernert, Jonathan A., Warm, Maximilian, Salvermoser, Lukas, Krüger, Nils, Bethe, Stephan, Kocheise, Lorenz, von Hake, Malte, Meyer-Schwickerath, Charlotte, Graupe, Tanja, Fischer, Martin R., Dimitriadis, Konstantinos
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 09.11.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Analysis
Career Planning
Content analysis
Data Analysis
Education
Evaluation
Medical Education
Medical mentoring
Medical students
Mentoring programs
Mentors
Methods
Online matching
Online mentoring profiles
Quality standards
Standard deviation
Technology application
Text analysis
Theory of Medicine/Bioethics
Vocational guidance
Germany
United States > US
Online matching
Medical mentoring
Online mentoring profiles
ISSN:1472-6920, 1472-6920
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Zusammenfassung:Background Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the mentor-selection. We aimed to qualitatively characterize mentoring-profile-texts, identify differences in form and content and thus elements that promote selection. Methods In a mixed method study first, quality of texts in 150 selected mentoring profiles was evaluated (10-point Likert scale; 1 = insufficient to 10 = very good). Second, based on a thematic and content analysis approach of profile texts, categories and subcategories were defined. We compared the presence of the assigned categories between the 25% highest ranked profiles with the 25% lowest ranked ones. Finally, additional predefined categories ( hot topics ) were labelled on the selected texts and their impact on student evaluation was statistically examined. Results Students rated the quality of texts with a mean of 5.89 ± 1.45. 5 main thematic categories, 21 categories and a total of 74 subcategories were identified. Ten subcategories were significantly associated with high- and four with low-rated profiles. The presence of three or more hot topics in texts significantly correlated with a positive evaluation. Conclusion The introduced classification system helps to understand how mentoring profile texts are composed and which aspects are important for choosing a suited mentor.
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ISSN:1472-6920
1472-6920
DOI:10.1186/s12909-023-04804-1