Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring
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Updated: Jul 11, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
Jonathan A Gernert1,2, Maximilian Warm1, Lukas Salvermoser1,3
1Institute of Medical Education, LMU University Hospital, Pettenkoferstr. 8a, Munich, 80336, Germany.
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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