Over a third of palliative medicine physicians meet burnout criteria: Results from a survey study during the COVID-19 pandemic
Bibliographic record
Abstract
Background:Palliative medicine physicians may be at higher risk of burnout due to increased stressors and compromised resilience during the COVID-19 pandemic. Burnout prevalence and factors influencing this among UK and Irish palliative medicine physicians is unknown.Aim:To determine the prevalence of burnout and the degree of resilience among UK and Irish palliative medicine physicians during the COVID-19 pandemic, and associated factors.Design:Online survey using validated assessment scales assessed burnout and resilience: The Maslach Burnout Inventory Human Services Survey for Medical Personnel [MBI-HSS (MP)] and the Connor-Davidson Resilience Scale (CD-RISC). Additional tools assessed depressive symptoms, alcohol use, and quality of life.Setting/participants:Association of Palliative Medicine of UK and Ireland members actively practising in hospital, hospice or community settings.Results:There were 544 respondents from the 815 eligible participants (66.8%), 462 provided complete MBI-HSS (MP) data and were analysed. Of those 181/462 (39.2%) met burnout criteria, based on high emotional exhaustion or depersonalisation subscales of the MBI-HSS (MP). A reduced odds of burnout was observed among physicians who worked ⩽20 h/week (vs 31–40 h/week, adjusted odds ratio (aOR) 0.03, 95% confidence interval (CI) 0.002–0.56) and who had a greater perceived level of clinical support (aOR 0.70, 95% CI 0.62–0.80). Physicians with higher levels of depressive symptoms had higher odds of burnout (aOR 18.32, 95% CI 6.75–49.73). Resilience, mean (SD) CD-RISC score, was lower in physicians who met burnout criteria compared to those who did not (62.6 (11.1) vs 70.0 (11.3); <i>p</i> < 0.001).Conclusions:Over one-third of palliative medicine physicians meet burnout criteria. The provision of enhanced organisational and colleague support is paramount in both the current and future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".