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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636985/ https://www.ncbi.nlm.nih.gov/pubmed/37946146 http://dx.doi.org/10.1186/s12909-023-04804-1 |
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author | 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 |
author_facet | 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 |
author_sort | Gernert, Jonathan A. |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04804-1. |
format | Online Article Text |
id | pubmed-10636985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106369852023-11-11 Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring 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 BMC Med Educ Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04804-1. BioMed Central 2023-11-09 /pmc/articles/PMC10636985/ /pubmed/37946146 http://dx.doi.org/10.1186/s12909-023-04804-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research 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 Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title | Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title_full | Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title_fullStr | Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title_full_unstemmed | Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title_short | Characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
title_sort | characteristics and quality assessment of online mentoring profile texts in academic medical mentoring |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636985/ https://www.ncbi.nlm.nih.gov/pubmed/37946146 http://dx.doi.org/10.1186/s12909-023-04804-1 |
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