Burnout in Greek Medical and Mental Health Care Workers
Bibliographic record
Abstract
Job Burnout affects job performance ability. Studies show a higher level of burnout in health professionals. The purpose of this study was to investigate possible differences in professional burnout subscales between health workers in medical and mental health sector. The sample constituted of randomly selected 240 workers in medical health sector and 217 in mental health sector, aged 39.8± 7.9 years old. Health workers from University and General Hospitals from all over Greece participated in the study. Maslach’s burnout inventory was used. SPSS 17.0 was used for statistics. The majority of health professionals were women. Over 50 % of workers in mental health sector showed low emotional exhaustion and depersonalization, while one third of them gave a high personal accomplishment score. Mental health professionals showed statistically significantly lower scores in emotional exhaustion and depersonalization , in comparison with medical sector workers. Different working environments influence the development of health care workers’ burnout
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".