IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i10p2190-d174109.html
   My bibliography  Save this article

The Issue of Burnout and Work Satisfaction in Younger GPs—A Cluster Analysis Utilizing the HaMEdSi Study

Author

Listed:
  • Oliver Hirsch

    (Department of Psychology, FOM University of Applied Sciences, 57078 Siegen, Germany)

  • Charles Christian Adarkwah

    (Department of Health Services Research and General Practice, Faculty of Life Sciences, University of Siegen, 57076 Siegen, Germany
    Department of General Practice and Family Medicine, Philipps-University, 35043 Marburg, Germany
    CAPHRI School for Public Health and Primary Care, Department of Health Services Research, Maastricht University, 6229 GT Maastricht, The Netherlands)

Abstract

The shortage of general practitioners (GPs) in Germany has become a relevant problem. Therefore, it is important to find the determinants that make primary care more attractive, and which support GPs remaining in practice. Our aim in this exploratory study was to search for relevant GP subgroups and their characteristics in order to find starting points for improvements or interventions. We attempted a comprehensive survey of all GPs in the German region of Siegen-Wittgenstein with about 280,000 inhabitants. There were 158 GPs in the total population; 85 of these (53.8%) took part in the study. There were 64 male GPs (75.3%) in our sample. The mean age of the participants was 53.5 years (SD 8.93). The questionnaire was composed of demographic questions, questions regarding future perspectives, the Motivation for Medical Education Questionnaire (MoME-Q), the Maslach Burnout Inventory (MBI), and the Work Satisfaction Questionnaire. K-means cluster analyses were used for subgrouping. A 2-cluster solution had good statistical quality criteria. Cluster 1 was characterised by elderly GPs who more frequently had a resident physician in their practices. These GPs had low burnout scores and high work satisfaction scores. Cluster 2 consisted of younger GPs who less frequently had a resident in their practices. They had average burnout scores according to published norms and lower work satisfaction scores. There seems to be an age cohort effect regarding burnout and work satisfaction. Having a resident physician seems to be protective. Interventions should be designed for younger GPs, especially members of generation Y, to reduce burnout and improve work satisfaction.

Suggested Citation

  • Oliver Hirsch & Charles Christian Adarkwah, 2018. "The Issue of Burnout and Work Satisfaction in Younger GPs—A Cluster Analysis Utilizing the HaMEdSi Study," IJERPH, MDPI, vol. 15(10), pages 1-10, October.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2190-:d:174109
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/10/2190/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/10/2190/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kowarik, Alexander & Templ, Matthias, 2016. "Imputation with the R Package VIM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i07).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Frédéric Dutheil & Lenise M. Parreira & Julia Eismann & François-Xavier Lesage & David Balayssac & Céline Lambert & Maëlys Clinchamps & Denis Pezet & Bruno Pereira & Bertrand Le Roy, 2021. "Burnout in French General Practitioners: A Nationwide Prospective Study," IJERPH, MDPI, vol. 18(22), pages 1-16, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Bram Janssens & Matthias Bogaert & Mathijs Maton, 2023. "Predicting the next Pogačar: a data analytical approach to detect young professional cycling talents," Annals of Operations Research, Springer, vol. 325(1), pages 557-588, June.
    3. Chhetri, Netra & Ghimire, Rajiv & Wagner, Melissa & Wang, Meng, 2020. "Global citizen deliberation: Case of world-wide views on climate and energy," Energy Policy, Elsevier, vol. 147(C).
    4. Ieva Burakauskaitė & Andrius Čiginas, 2023. "An Approach to Integrating a Non-Probability Sample in the Population Census," Mathematics, MDPI, vol. 11(8), pages 1-14, April.
    5. Carlos Miguel Lemos & Ross Joseph Gore & Ivan Puga-Gonzalez & F LeRon Shults, 2019. "Dimensionality and factorial invariance of religiosity among Christians and the religiously unaffiliated: A cross-cultural analysis based on the International Social Survey Programme," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-36, May.
    6. Nicholas Tierney & Dianne Cook, 2018. "Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations," Monash Econometrics and Business Statistics Working Papers 14/18, Monash University, Department of Econometrics and Business Statistics.
    7. Sonja Herrmann & Christian Nagel, 2023. "Early Careers of Graduates from Private and Public Universities in Germany: A Comparison of Income Differences Regarding the First Employment," Research in Higher Education, Springer;Association for Institutional Research, vol. 64(1), pages 129-146, February.
    8. Ahmad R. Alsaber & Jiazhu Pan & Adeeba Al-Hurban, 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    9. Marc Kuhn & Viola Marquardt & Sarah Selinka, 2021. "“Is Sharing Really Caring?”: The Role of Environmental Concern and Trust Reflecting Usage Intention of “Station-Based” and “Free-Floating”—Carsharing Business Models," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    10. Elena Escolano-Pérez & Marta Bestué, 2021. "Academic Achievement in Spanish Secondary School Students: The Inter-Related Role of Executive Functions, Physical Activity and Gender," IJERPH, MDPI, vol. 18(4), pages 1-25, February.
    11. Zhixin Lun & Ravindra Khattree, 2021. "Imputation for Skewed Data: Multivariate Lomax Case," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 86-113, May.
    12. Nengsih Titin Agustin & Bertrand Frédéric & Maumy-Bertrand Myriam & Meyer Nicolas, 2019. "Determining the number of components in PLS regression on incomplete data set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-28, December.
    13. Frank Tian-Fang Ye & Kuen-Fung Sin & Xiaozi Gao, 2021. "Subjective Well-Being among Parents of Children with Special Educational Needs in Hong Kong: Impacts of Stigmatized Identity and Discrimination under Social Unrest and COVID-19," IJERPH, MDPI, vol. 19(1), pages 1-13, December.
    14. Maciej Berk{e}sewicz & Greta Bia{l}kowska & Krzysztof Marcinkowski & Magdalena Ma'slak & Piotr Opiela & Robert Pater & Katarzyna Zadroga, 2019. "Enhancing the Demand for Labour survey by including skills from online job advertisements using model-assisted calibration," Papers 1908.06731, arXiv.org.
    15. Maciej Beręsewicz & Dagmara Nikulin, 2018. "Informal employment in Poland: an empirical spatial analysis," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(3), pages 338-355, July.
    16. Schalk Burger & Searle Silverman & Gary van Vuuren, 2018. "Deriving Correlation Matrices for Missing Financial Time-Series Data," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 105-105, October.
    17. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    18. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
    19. Matthias Templ, 2023. "Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    20. P. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2017. "Imputation Based Treatment Effect Estimators," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(3), pages 1-2.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:10:p:2190-:d:174109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.