Fully Developed Burnout and Burnout Risk in Intensive Care Personnel at a University Hospital
Bibliographic record
Abstract
We assessed the prevalence of fully developed burnout, burnout risk and the influence of work and employment related factors in five intensive care units at a university hospital. A cross-sectional study was conducted using self-reporting questionnaires for the evaluation of the frequency and intensity of burnout syndrome (Maslach Burnout Inventory) and work and employment related factors. From a total of 320 eligible intensive care personnel, 33 physicians and 150 nurses participated in the study (59% response rate). Applying the process model for burnout, 63 participants (34.4%) were at risk for burnout and another 11 respondents (6.0%) revealed evidence of fully developed burnout (emotional exhaustion > or =4.0 and lack of personal accomplishment < or =4.0). No statistically significant difference in prevalence of fully developed burnout or burnout risk was detected in sub-groups according to age, gender level of training, years of employment and family status. The desire to choose the same profession again was significantly less in respondents with fully developed burnout (P=0.006). The opportunity to regularly attend facilitation was significantly lower for participants with fully developed burnout (P=0.002) compared to participants with no burnout. Fully developed burnout and burnout risk are common in intensive care personnel. Support from facilitators appeared to be an important preventive factor
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".