What Pushes University Professors to Burnout? A Systematic Review of Sociodemographic and Psychosocial Determinants
Bibliographic record
Abstract
Burnout syndrome is a growing concern in higher education, affecting the psychological well-being and performance of university professors. This systematic review presents a narrative synthesis of findings from quantitative studies on sociodemographic and psychosocial determinants of academic burnout. Following PRISMA 2020 guidelines, sixty peer-reviewed articles published between Jan 2019 and May 2024 were selected from Scopus and Web of Science. Inclusion criteria required validated psychometric instruments and exclusive focus on university faculty. Methodological quality was assessed using the Newcastle-Ottawa Scale and CASP checklist. Data from approximately 43,639 academic staff were analyzed. Key risk factors identified include excessive workload, lack of institutional support, and workplace conflict. In contrast, collegial support, participative leadership, and job satisfaction functioned as protective elements. Variables such as age, gender, academic rank, and employment stability significantly influenced burnout vulnerability. While general patterns were observed across studies, differences in design and sampling require caution in generalization. The evidence supports the implementation of integrated strategies encompassing mental health programs, workload regulation, participatory governance, and culturally responsive approaches. These findings inform the development of institutional policies aimed at preventing burnout and fostering academic well-being. Future research should adopt longitudinal and cross-cultural designs to further explore burnout trajectories and support educational reform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".