Women healthcare workers’ experiences during COVID-19 and other crises: A scoping review
Bibliographic record
Abstract
BACKGROUND: Throughout the COVID-19 pandemic, as measures have been taken to both prevent the spread of COVID-19 and provide care to those who fall ill, healthcare workers have faced added risks to their health and wellbeing. These risks are disproportionately felt by women healthcare workers, yet health policies do not always take a gendered approach. OBJECTIVES: The objective of this review was to identify the gendered effects of crises on women healthcare workers' health and wellbeing, as well as to provide guidance for decision-makers on health systems policies and programs that could better support women healthcare workers. METHODS: A scoping review of published academic literature was conducted. PubMed, EMBASE, and CINAHL were searched using combinations of relevant medical subject headings and keywords. Data was extracted using a thematic coding framework. Seventy-six articles met the inclusion criteria. RESULTS: During disease outbreaks women healthcare workers were found to experience: a higher risk of exposure and infection; barriers to accessing personal protective equipment; increased workloads; decreased leadership and decision-making opportunities; increased caregiving responsibilities in the home when schools and childcare supports were restricted; and higher rates of mental ill-health, including depression, anxiety, and post-traumatic stress disorder. There was a lack of attention paid to gender and the health workforce during times of crisis prior to COVID-19, and there is a substantial gap in research around the experiences of women healthcare workers in low- and middle-income countries during times of crises. CONCLUSION: COVID-19 provides an opportunity to develop gender-responsive crisis preparedness plans within the health sector. Without consideration of gender, crises will continue to exacerbate existing gender disparities, resulting in disproportionate negative impacts on women healthcare workers. The findings point to several important recommendations to better support women healthcare workers, including: workplace mental health support, economic assistance to counteract widening pay gaps, strategies to support their personal caregiving duties, and interventions that support and advance women's careers and increase their representation in leadership roles.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| 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".