Trends in burnout and psychological distress in hospital staff over 12 months of the COVID-19 pandemic: a prospective longitudinal survey
Bibliographic record
Abstract
BACKGROUND: The mental health effects of healthcare work during the COVID-19 pandemic have been substantial, but it is not known how long they will persist. This study aimed to determine if hospital workers' burnout and psychological distress increased monotonically over 1 year, during which waves of case numbers and hospitalizations waxed and waned, or followed some other pattern. METHODS: A prospective longitudinal survey was conducted at four time-points over 1 year in a cohort of 538 hospital workers and learners, which included validated measures of burnout (emotional exhaustion scale of Maslach Burnout Inventory) and psychological distress (K6). Repeated measures ANOVA tested changes over time and differences between subjects by occupational role, age and ethnic group. The direction and magnitude of changes over time were investigated by plotting rates of high scores (using cut-offs) at each time-point compared to case rates of COVID-19 in the city in which the study took place. RESULTS: There were significant effects of occupational role (F = 11.2, p < .001) and age (F = 12.8, p < .001) on emotional exhaustion. The rate of high burnout was highest in nurses, followed by other healthcare professionals, other clinical staff, and lowest in non-clinical staff. Peak rates of high burnout occurred at the second or third measurement point for each occupational group, with lower rates at the fourth measurement point. Similarly to the results for emotional exhaustion, rates of high psychological distress peaked at the spring 2021 measurement point for most occupational groups and were higher in nurses than in other healthcare professionals. CONCLUSIONS: Neither emotional exhaustion nor psychological distress was rising monotonically. Burnout and psychological distress were consistently related to occupational role and were highest in nurses. Although emotional exhaustion improved as the case rate of COVID-19 decreased, rates of high emotional exhaustion in nurses and other healthcare professionals remained higher than was typically measured in hospital-based healthcare workers prior to the pandemic. Ongoing monitoring of healthcare workers' mental health is warranted. Organizational and individual interventions to support healthcare workers continue to be important.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".