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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Vydané v:	BMC medical education Ročník 23; číslo 1; s. 1 - 8
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 09.11.2023 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	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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Abstract	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.
AbstractList	BackgroundMentoring 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.MethodsIn 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.ResultsStudents 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.ConclusionThe introduced classification system helps to understand how mentoring profile texts are composed and which aspects are important for choosing a suited mentor. 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. 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.BACKGROUNDMentoring 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.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.METHODSIn 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.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.RESULTSStudents 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.The introduced classification system helps to understand how mentoring profile texts are composed and which aspects are important for choosing a suited mentor.CONCLUSIONThe introduced classification system helps to understand how mentoring profile texts are composed and which aspects are important for choosing a suited mentor. Abstract 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. 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. 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. Students rated the quality of texts with a mean of 5.89 [+ or -] 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. The introduced classification system helps to understand how mentoring profile texts are composed and which aspects are important for choosing a suited mentor. 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 [+ or -] 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. Keywords: Medical mentoring, Online matching, Online mentoring profiles
ArticleNumber	849
Audience	Academic
Author	Bethe, Stephan Salvermoser, Lukas Krüger, Nils Fischer, Martin R. Dimitriadis, Konstantinos Gernert, Jonathan A. Warm, Maximilian Kocheise, Lorenz von Hake, Malte Graupe, Tanja Meyer-Schwickerath, Charlotte
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Snippet	Background Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the... Background Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the... Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the mentor-selection. We... BackgroundMentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the... Abstract Background Mentoring is important for a successful career in academic medicine. In online matching processes, profile texts are decisive for the...
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SubjectTerms	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
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Title	Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring
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