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 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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
BMC medical education - 23(2023), 1 vom: 09. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gernert, Jonathan A. [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| Anmerkungen: |
© The Author(s) 2023 |
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| doi: |
10.1186/s12909-023-04804-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR053683099 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Medical mentoring |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Online matching |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Online mentoring profiles |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Warm, Maximilian |4 aut | |
| 700 | 1 | |a Salvermoser, Lukas |4 aut | |
| 700 | 1 | |a Krüger, Nils |4 aut | |
| 700 | 1 | |a Bethe, Stephan |4 aut | |
| 700 | 1 | |a Kocheise, Lorenz |4 aut | |
| 700 | 1 | |a von Hake, Malte |4 aut | |
| 700 | 1 | |a Meyer-Schwickerath, Charlotte |4 aut | |
| 700 | 1 | |a Graupe, Tanja |4 aut | |
| 700 | 1 | |a Fischer, Martin R. |4 aut | |
| 700 | 1 | |a Dimitriadis, Konstantinos |4 aut | |
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