Sophie Berjot , Emin Altintas , Elisabeth Grebot , François-Xavier Lesage
{"title":"法国心理学家的职业倦怠风险概况","authors":"Sophie Berjot , Emin Altintas , Elisabeth Grebot , François-Xavier Lesage","doi":"10.1016/j.burn.2017.10.001","DOIUrl":null,"url":null,"abstract":"<div><p>The aims of this study were 1) to show that the use of different cut-off scores available in the literature can lead to erroneous conclusions, adding to the emerging literature highlighting the problems associated with its use, and 2) to propose an alternative technique − Cluster Analysis − to assess the risk of burnout as well as to identify profiles at risk of burnout.</p><p>Burnout was measured among 664 French psychologists using the French-Canadian version of the Maslach Burnout Inventory (<span>Dion & Tessier, 1994</span>). Our participants were classified as high on each dimension of the MBI using different cut-off scores available in the literature and using the Cluster Analysis method.</p><p>The study showed that the use of cut-off scores can indeed be misleading as conclusions may be very different according to the cut-off used. Cluster analysis allowed us to highlight four distinct burnout risk profiles: “High risk of burnout”, “Risk of burnout through high emotional exhaustion”, “Risk of burnout through low personal accomplishment”, and “No risk of burnout”. Several variables appeared as predictors of occupational burnout such as working in a company or having several different types of contracts, showing the discriminative power of clusters. Finally, a discussion is proposed on the meaning of the identified clusters and the use of this analysis in research and practice.</p></div>","PeriodicalId":90459,"journal":{"name":"Burnout research","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.burn.2017.10.001","citationCount":"37","resultStr":"{\"title\":\"Burnout risk profiles among French psychologists\",\"authors\":\"Sophie Berjot , Emin Altintas , Elisabeth Grebot , François-Xavier Lesage\",\"doi\":\"10.1016/j.burn.2017.10.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The aims of this study were 1) to show that the use of different cut-off scores available in the literature can lead to erroneous conclusions, adding to the emerging literature highlighting the problems associated with its use, and 2) to propose an alternative technique − Cluster Analysis − to assess the risk of burnout as well as to identify profiles at risk of burnout.</p><p>Burnout was measured among 664 French psychologists using the French-Canadian version of the Maslach Burnout Inventory (<span>Dion & Tessier, 1994</span>). Our participants were classified as high on each dimension of the MBI using different cut-off scores available in the literature and using the Cluster Analysis method.</p><p>The study showed that the use of cut-off scores can indeed be misleading as conclusions may be very different according to the cut-off used. Cluster analysis allowed us to highlight four distinct burnout risk profiles: “High risk of burnout”, “Risk of burnout through high emotional exhaustion”, “Risk of burnout through low personal accomplishment”, and “No risk of burnout”. Several variables appeared as predictors of occupational burnout such as working in a company or having several different types of contracts, showing the discriminative power of clusters. Finally, a discussion is proposed on the meaning of the identified clusters and the use of this analysis in research and practice.</p></div>\",\"PeriodicalId\":90459,\"journal\":{\"name\":\"Burnout research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.burn.2017.10.001\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Burnout research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213058617300189\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Burnout research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213058617300189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The aims of this study were 1) to show that the use of different cut-off scores available in the literature can lead to erroneous conclusions, adding to the emerging literature highlighting the problems associated with its use, and 2) to propose an alternative technique − Cluster Analysis − to assess the risk of burnout as well as to identify profiles at risk of burnout.
Burnout was measured among 664 French psychologists using the French-Canadian version of the Maslach Burnout Inventory (Dion & Tessier, 1994). Our participants were classified as high on each dimension of the MBI using different cut-off scores available in the literature and using the Cluster Analysis method.
The study showed that the use of cut-off scores can indeed be misleading as conclusions may be very different according to the cut-off used. Cluster analysis allowed us to highlight four distinct burnout risk profiles: “High risk of burnout”, “Risk of burnout through high emotional exhaustion”, “Risk of burnout through low personal accomplishment”, and “No risk of burnout”. Several variables appeared as predictors of occupational burnout such as working in a company or having several different types of contracts, showing the discriminative power of clusters. Finally, a discussion is proposed on the meaning of the identified clusters and the use of this analysis in research and practice.