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Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Related Experiment Videos

Multidomain correlates of burnout: A population-based study using supervised machine learning.

Anja Monstadt1,2, Yvonne Friedrich3, Fabian Rottstädt3,4

  • 1Department of Clinical Psychology, Friedrich-Schiller-Universität Jena, Jena, Germany. anja.monstadt@uni-jena.de.

Social Psychiatry and Psychiatric Epidemiology
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Burnout is significantly predicted by poor work-life balance and mental health issues like depression and anxiety. Positive work environments may prevent burnout, while existing mental health problems increase vulnerability.

Keywords:
BurnoutMental HealthOrganisational PsychologySupervised Machine Learning

Related Experiment Videos

Area of Science:

  • Occupational Health
  • Psychology
  • Sociology

Background:

  • Burnout is a significant occupational health concern with increasing societal impact.
  • Understanding burnout risk factors is crucial for developing effective prevention strategies.

Purpose of the Study:

  • To investigate the relationship between burnout and organizational factors, psychosocial employment conditions, sociodemographic variables, and mental health.
  • To identify key predictors of burnout severity using advanced machine learning techniques.

Main Methods:

  • Analysis of cross-sectional survey data from the German population-based DigiHero cohort (n=27,020).
  • Utilized linear associations and XGBoost machine learning to explore multidomain variable relationships with burnout, measured by the Maslach Burnout Inventory.
  • Interpreted machine learning results using SHAP values and compared with univariate statistics, adjusting for sample bias.

Main Results:

  • Effort-reward imbalance, work-life interference, overcommitment, and poor general mental health (depression, anxiety symptoms) were the strongest burnout predictors.
  • Psychosocial employment conditions and individual mental health were more influential than organizational and sociodemographic factors.
  • Occupation, extended remote work, age, and income showed smaller but notable effects on burnout.

Conclusions:

  • Psychosocial employment conditions and individual mental health are paramount in burnout etiology, overshadowing organizational and sociodemographic factors.
  • Positive social work environments are suggested for burnout prevention, while pre-existing mental health issues heighten vulnerability.
  • Prospective longitudinal studies are recommended for deeper causal insights into burnout development.