Frequency of burnout syndrome among nurses at the health center in Sremska Mitrovica
Bibliographic record
Abstract
Background: When it comes to professional stress, the work environment is one of its most important sources. Today, great attention is paid to researching the specifics of professional stress. Its sources and consequences for physical and mental health, as well as work productivity, are particularly important. Aim: To examine how many nurses and medical technicians employed at the Sremska Mitrovica Health Center are affected by burnout syndrome. Materials and Methods: A cross-sectional, descriptive study was conducted using the Burnout Syndrome Intensity at Work questionnaire, which is publicly available under the name "Burnout Syndrome" - HDOD. The study included 50 nurses and medical technicians employed at the Health Center in Sremska Mitrovica. The research was conducted during June and July 2025. Results: Of the total number of respondents, burnout syndrome was present in 14%. Additionally, 2% of respondents were already burned out at work, 38% were at risk of burnout syndrome, 26% were at risk, and 20% reported feeling well. No statistically significant correlation was found between gender, education level, workplace, and subjective feelings of burnout. A statistically significant positive correlation was found between respondents' age and the length of their work experience. Conclusion: Nurses and medical technicians are exposed to relatively high levels of stress. Burnout syndrome represents not only a psychological but also a physical disorder; therefore, it is important to recognize its onset in time before it seriously threatens health, daily functioning, and work productivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".