BURNOUT SYNDROM AMONG PUBLIC AMBULANCE STAFF.
Bibliographic record
Abstract
UNLABELLED: Healthcare professionals are frequently confronted with urgent situations and a high-risk human intervention. They are usually exposed to what is called burnout syndrome. AIM: To identify the effects of burnout syndrome on the professional conduct and attitudes of doctors and nurses who work in the Romanian public ambulance service. Secondary, the causal relationships between burnout and various socio-demographic variables were analyzed. MATERIAL AND METHODS: The 20-item Toronto Alexithymia Scale (TAS- 20), Maslach Burnout Inventory and Job Satisfaction Questionnaire were administered to 122 ambulance doctors, nurses and drivers (62 females and 60 males). RESULTS: The degree of job satisfaction is the most important indicator of burnout syndrome. Significant differences were found between low and high alexithymic subjects. Women are more susceptible to experience higher levels of burnout than men. The level of burnout is influenced by the combined effect of job satisfaction and alexithymia. CONCLUSIONS: Burnout syndrome is a common problem among people working in the emergency medical system. The causes of job-related burnout have to be identified in order to apply an appropriate level of burnout intervention program and to increase the efficiency of coping strategies.
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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 | 0.000 | 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) | 0.002 | 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".