Experiences and management of physician psychological symptoms during infectious disease outbreaks: a rapid review
Bibliographic record
Abstract
BACKGROUND: Prior to the COVID-19 pandemic, physicians experienced unprecedented levels of burnout. The uncertainty of the ongoing COVID-19 pandemic along with increased workload and difficult medical triage decisions may lead to a further decline in physician psychological health. METHODS: We searched Medline, EMBASE, and PsycINFO for primary research from database inception (Medline [1946], EMBASE [1974], PsycINFO [1806]) to November 17, 2020. Titles and abstracts were screened by one of three reviewers and full-text article screening and data abstraction were conducted independently, and in duplicate, by three reviewers. RESULTS: From 6223 unique citations, 480 articles were reviewed in full-text, with 193 studies (of 90,499 physicians) included in the final review. Studies reported on physician psychological symptoms and management during seven infectious disease outbreaks (severe acute respiratory syndrome [SARS], three strains of Influenza A virus [H1N1, H5N1, H7N9], Ebola, Middle East respiratory syndrome [MERS], and COVID-19) in 57 countries. Psychological symptoms of anxiety (14.3-92.3%), stress (11.9-93.7%), depression (17-80.5%), post-traumatic stress disorder (13.2-75.2%) and burnout (14.7-76%) were commonly reported among physicians, regardless of infectious disease outbreak or country. Younger, female (vs. male), single (vs. married), early career physicians, and those providing direct care to infected patients were associated with worse psychological symptoms. INTERPRETATION: Physicians should be aware that psychological symptoms of anxiety, depression, fear and distress are common, manifest differently and self-management strategies to improve psychological well-being exist. Health systems should implement short and long-term psychological supports for physicians caring for patients with COVID-19.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".