Burnout Syndrome in UK Intensive Care Unit staff: Data from all three Burnout Syndrome domains and across professional groups, genders and ages
Bibliographic record
Abstract
INTRODUCTION: This is the first comprehensive evaluation of Burnout Syndrome across the UK Intensive Care Unit workforce and in all three Burnout Syndrome domains: Emotional Exhaustion, Depersonalisation and lack of Personal Accomplishment. METHODS: A questionnaire was emailed to UK Intensive Care Society members, incorporating the 22-item Maslach Burnout Inventory Human Services Survey for medical personnel. Burnout Syndrome domain scores were stratified by 'risk'. Associations with gender, profession and age-group were explored. RESULTS: In total, 996 multi-disciplinary responses were analysed. For Emotional Exhaustion, females scored higher and nurses scored higher than doctors. For Depersonalisation, males and younger respondents scored higher. CONCLUSION: Approximately one-third of Intensive Care Unit team-members are at 'high-risk' for Burnout Syndrome, though there are important differences according to domain, gender, age-group and profession. This data may encourage a more nuanced understanding of Burnout Syndrome and more personalised strategies for our heterogeneous workforce.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".