The Impact of COVID-19 on Physician Burnout Globally: A Review
Bibliographic record
Abstract
Background: The current pandemic, COVID-19, has added to the already high levels of stress that medical professionals face globally. While most health professionals have had to shoulder the burden, physicians are not often recognized as being vulnerable and hence little attention is paid to morbidity and mortality within this group. Objective: To analyse and summarise the current knowledge on factors/potential factors contributing to burnout amongst healthcare professionals amidst the pandemic. This review also makes a few recommendations on how best to prepare intervention programmes for physicians. Methods: In August 2020, a systematic review was performed using the database Medline and Embase (OVID) to search for relevant papers on the impact of COVID-19 on physician burnout–the database was searched for terms such as “COVID-19 OR pandemic” AND “burnout” AND “healthcare professional OR physician”. A manual search was done for other relevant studies included in this review. Results: Five primary studies met the inclusion criteria. A further nine studies were included which evaluated the impact of occupational factors (n = 2), gender differences (n = 4) and increased workload/sleep deprivation (n = 3) on burnout prior to the pandemic. Additionally, five reviews were analysed to support our recommendations. Results from the studies generally showed that the introduction of COVID-19 has heightened existing challenges that physicians face such as increasing workload, which is directly correlated with increased burnout. However, exposure to COVID-19 does not necessarily correlate with increased burnout and is an area for more research. Conclusions: There is some evidence showing that techniques such as mindfulness may help relieve burnout. However, given the small number of studies focusing on physician burnout amidst a pandemic, conclusions should be taken with caution. More studies are needed to support these findings.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".