Women's World Majlis | Women on the Frontline or In the Line of Fire?

Women's World Majlis

Women on the Frontline

The COVID-19 crisis has highlighted existing (gender) inequalities in healthcare professions as well as the acute challenges of being a frontline female healthcare worker. These range from rising work pressure due to understaffing, to increased violence, to underrepresentation in decision-making and a wide variety of gender biases at the institutional, executive and patient-level. Healthcare workers are the beating heart of health system across the work. The vast majority of these professionals, around 70 percent, is female. Global demand for health workers is on the rise, but at the same time, the profession is becoming rapidly less attractive. By 2030, we might see a shortfall of 18 million healthcare workers, according to WHO estimates. While the problem is acute across the world, it is most pronounced in low- and lower-middle income countries. Frontline healthcare workers risk their lives to save others and deserve all our support. But while the pandemic response has been combined with outbursts of public support for those saving lives, their day-to-day reality remains full of challenges. The most pressing of these might be that of growing violence against healthcare workers. To illustrate, between 2016 and 2020, the International Committee of the Red Cross (ICRC) counted no less than 3,780 attacks on healthcare workers and facilities. Most likely, with most cases unrecorded, this is only the “tip of the iceberg”. But the increase in work floor violence is just one of the many challenges female frontline workers face. There are many other stressors, which are less visible and as such, often perceived as less acute. These include underrepresentation in decision-making, with just 30% of all C-suite executives across the health-industry being women3, as well as double care duties when domestic housework also falls on these professionals. Another deep-rooted problem is that of implicit gender-career biases.

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