A network analysis bridging the gap between the big five personality traits and burnout among medical staff

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Abstract

Background

Burnout is a common issue among medical professionals, and one of the well-studied predisposing factors is the Big Five personality traits. However, no studies have explored the relationships between these traits and burnout from a trait-to-component perspective. To understand the specific connections between each Big Five trait and burnout components, as well as the bridging effects of each trait on burnout, we employed network analysis.

Methods

A cluster sampling method was used to select a total of 420 Chinese medical personnel. The 15-item Chinese Big Five Personality Inventory-15 (CBF-PI-15) assessed the Big Five personality traits, while the 15-item Maslach Burnout Inventory-General Survey (MBI-GS) assessed burnout components. Network analysis was used to estimate network structure of Big Five personality traits and burnout components and calculate the bridge expected influence.

Results

The study revealed distinct and clear relationships between the Big Five personality traits and burnout components. For instance, Neuroticism was positively related to Doubt significance and Worthwhile, while Conscientiousness was negatively related to Accomplish all tasks. Among the Big Five traits, Neuroticism displayed the highest positive bridge expected influence, while Conscientiousness displayed the highest negative bridge expected influence.

Conclusions

The network model provides a means to investigate the connections between the Big Five personality traits and burnout components among medical professionals. This study offers new avenues for thought and potential targets for burnout prevention and treatment in medical personnel, which can be further explored and tested in clinical settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-024-01751-0.

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Most cited references 51

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The measurement of experienced burnout

Christina Maslach, Susan Jackson (1981)

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Job burnout.

C Maslach, W B Schaufeli, M P Leiter (2001)

Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job, and is defined by the three dimensions of exhaustion, cynicism, and inefficacy. The past 25 years of research has established the complexity of the construct, and places the individual stress experience within a larger organizational context of people's relation to their work. Recently, the work on burnout has expanded internationally and has led to new conceptual models. The focus on engagement, the positive antithesis of burnout, promises to yield new perspectives on interventions to alleviate burnout. The social focus of burnout, the solid research basis concerning the syndrome, and its specific ties to the work domain make a distinct and valuable contribution to people's health and well-being.

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Estimating psychological networks and their accuracy: A tutorial paper

Sacha Epskamp, Denny Borsboom, Eiko I Fried (2017)

The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.

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Author and article information

Contributors

Lei Ren: rl_fmmu@163.com

Xufeng Liu: lxf_fmmu@163.com

Journal

Journal ID (nlm-ta): BMC Nurs

Journal ID (iso-abbrev): BMC Nurs

Title: BMC Nursing

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6955

Publication date (Electronic): 4 February 2024

Publication date PMC-release: 4 February 2024

Publication date Collection: 2024

Volume: 23

Electronic Location Identifier: 92

Affiliations

[1 ]Department of Military Medical Psychology, Air Force Medical University, ( https://ror.org/00ms48f15) 169 Street, 710032 Xi’an, China

[2 ]BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, ( https://ror.org/02bfwt286) 3168 Clayton, Australia

[3 ]Department of Psychology, Army Medical University, ( https://ror.org/05w21nn13) 400038 Chongqing, China

[4 ]Department of infectious diseases, Juxian Hospital of Traditional Chinese Medicine, Shandong Traditional Chinese Medicine University, 23 Street, 276500 Rizhao, China

[5 ]Department of Foreign Language Teaching and Research of Basic Ministry, Air Force Medical University, ( https://ror.org/00ms48f15) 169 Street, 710032 Xi’an, China

[6 ]School of Nursing, Air Force Medical University, ( https://ror.org/00ms48f15) 169 Street, 710032 Xi’an, China

[7 ]Military Psychology Section, Logistics University of PAP, ( https://ror.org/02syyrn67) 300309 Tianjin, China

[8 ]Military Mental Health Services & Research Center, 300309 Tianjin, China

Article

Publisher ID: 1751

DOI: 10.1186/s12912-024-01751-0

PMC ID: 10838458

PubMed ID: 38311767

SO-VID: 7caeb9c0-5d02-43fd-9592-3dacc87b8965

License:

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/. The Creative Commons Public Domain Dedication waiver ( http://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.

History

Date received : 2 September 2023

Date accepted : 21 January 2024

Funding

Funded by: The Key Project of Air Force Equipment Comprehensive Research

Award ID: KJ2022A000415

Funded by: Research on the characteristics of attention network based on multi-modal indicators

Award ID: 2023KXKT061

Funded by: The Development Mechanism and Adustment Strategy of Nursing Staff Burnout in the post-epidemic Era

Award ID: 2023KXKT018

Custom metadata

ScienceOpen disciplines: Nursing

Keywords: big five personality traits,burnout,medical staff,network analysis

Data availability:

ScienceOpen disciplines: Nursing

Keywords: big five personality traits, burnout, medical staff, network analysis

A network analysis bridging the gap between the big five personality traits and burnout among medical staff

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Related collections

Bulletin Of Medical And Biological Research

Most cited references 51

The measurement of experienced burnout

Job burnout.

Estimating psychological networks and their accuracy: A tutorial paper

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Contributors

Journal

Affiliations

Article

History

Funding

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