To burn-out or not to burn-out: a cross-sectional study in healthcare professionals in Spain during COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of burn-out syndrome in healthcare workers working on the front line (FL) in Spain during COVID-19. DESIGN: Cross-sectional, online survey-based study. SETTINGS: Sampling was performed between 21st April and 3rd May 2020. The survey collected demographic data and questions regarding participants' working position since pandemic outbreak. PARTICIPANTS: Spanish healthcare workers working on the FL or usual ward were eligible. A total of 674 healthcare professionals answered the survey. MAIN OUTCOMES AND MEASURES: Burn-out syndrome was assessed by the Maslach Burnout Inventory-Medical Personnel. RESULTS: Of the 643 eligible responding participants, 408 (63.5%) were physicians, 172 (26.8%) were nurses and 63 (9.8%) other technical occupations. 377 (58.6%) worked on the FL. Most participants were women (472 (73.4%)), aged 31-40 years (163 (25.3%)) and worked in tertiary hospitals (>600 beds) (260 (40.4%)). Prevalence of burn-out syndrome was 43.4% (95% CI 39.5% to 47.2%), higher in COVID-19 FL workers (49.6%, p<0.001) than in non- COVID-19 FL workers (34.6%, p<0.001). Women felt more burn-out (60.8%, p=0.016), were more afraid of self-infection (61.9%, p=0.021) and of their performance and quality of care provided to the patients (75.8%, p=0.015) than men. More burn-out were those between 20 and 30 years old (65.2%, p=0.026) and those with more than 15 years of experience (53.7%, p=0.035).Multivariable logistic regression analysis revealed that, working on COVID-19 FL (OR 1.93; 95% CI 1.37 to 2.71, p<0.001), being a woman (OR 1.56; 95% CI 1.06 to 2.29, p=0.022), being under 30 years old (OR 1.75; 95% CI 1.06 to 2.89, p=0.028) and being a physician (OR 1.64; 95% CI 1.11 to 2.41, p=0.011) were associated with high risk of burn-out syndrome. CONCLUSIONS: This survey study of healthcare professionals reported high rates of burn-out syndrome. Interventions to promote mental well-being in healthcare workers exposed to COVID-19 need to be immediately implemented.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".