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Burnout Risk Profiles in Psychology Students: An Exploratory Study with Machine Learning.

M Graça Pereira1, Martim Santos1, Renata Magalhães2

  • 1Psychology Research Centre (CIPsi), School of Psychology, University of Minho, 4710-057 Braga, Portugal.

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|April 26, 2025
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Summary

University students face burnout risks. This study identified key psychological and lifestyle factors linked to academic burnout in psychology students, enabling risk profiling and prevention strategies.

Keywords:
burnoutemotional regulationhealthy lifestylesmachine learningpsychological distresspsychological well-beingrisk profilesuniversity students

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Area of Science:

  • Psychology
  • Public Health
  • Educational Psychology

Background:

  • University students experience high academic workloads and performance expectations, increasing their risk of burnout and psychological distress.
  • Academic burnout is a significant concern in higher education, impacting student well-being and academic success.

Purpose of the Study:

  • To analyze the relationship between psychological and lifestyle variables and academic burnout in Portuguese psychology students.
  • To identify distinct burnout risk profiles among psychology students using machine learning.

Main Methods:

  • Cross-sectional study involving 274 Portuguese psychology students (72.6% undergraduates).
  • Assessment of psychological well-being, distress, emotional regulation, diet, physical activity, sleep quality, and burnout.
  • Application of machine learning algorithms (Random Forest, XGBoost, SVM) to identify burnout risk profiles.

Main Results:

  • Psychological distress, emotional regulation difficulties, and poor sleep quality were positively associated with burnout.
  • Psychological well-being was negatively associated with burnout.
  • Two profiles identified: 'Burnout Risk' (62 participants) and 'No Risk'. The 'Burnout Risk' profile exhibited higher distress, emotional difficulties, lower well-being and sleep quality, pro-inflammatory diet, and less physical activity. Machine learning models achieved high accuracy (93.82%–97.53%).

Conclusions:

  • Psychological distress, emotional regulation, and sleep quality are critical factors in student burnout.
  • Machine learning effectively identifies distinct burnout risk profiles in university students.
  • Health promotion and targeted mental health strategies are crucial for preventing academic burnout in university settings.