Women in healthcare experiencing occupational stress and burnout during COVID-19: a rapid review
Bibliographic record
Abstract
Context: COVID-19 has had an unprecedent impact on physicians, nurses and other health professionals around the world, and a serious healthcare burnout crisis is emerging as a result of this pandemic. Objectives: We aim to identify the causes of occupational stress and burnout in women in medicine, nursing and other health professions during the COVID-19 pandemic and interventions that can support female health professionals deal with this crisis through a rapid review. Methods: We searched MEDLINE, Embase, CINAHL, PsycINFO and ERIC from December 2019 to 30 September 2020. The review protocol was registered in PROSPERO and is available online. We selected all empirical studies that discussed stress and burnout in women healthcare workers during the COVID-19 pandemic. Results: The literature search identified 6148 citations. A review of abstracts led to the retrieval of 721 full-text articles for assessment, of which 47 articles were included for review. Our findings show that concerns of safety (65%), staff and resource adequacy (43%), workload and compensation (37%) and job roles and security (41%) appeared as common triggers of stress in the literature. Conclusions and relevance: The current literature primarily focuses on self-focused initiatives such as wellness activities, coping strategies, reliance of family, friends and work colleagues to organisational-led initiatives such as access to psychological support and training. Very limited evidence exists about the organisational interventions such as work modification, financial security and systems improvement.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".