Mental Health and Well-Being Needs among Non-Health Essential Workers during Recent Epidemics and Pandemics.
Nashit ChowdhuryAnkit KainthAtobrhan GodluHoney Abigail FarinasSaif SikdarTanvir Chowdhury TurinPublished in: International journal of environmental research and public health (2022)
Essential workers, those who work in a variety of sectors that are critical to sustain the societal infrastructure, were affected both physically and mentally by the COVID-19 pandemic. While the most studied group of this population were healthcare workers, other essential non-health workers such as those working in the law enforcement sector, grocery services, food services, delivery services, and other sectors were studied less commonly. We explored both the academic (using MEDLINE, PsycInfo, CINAHL, Sociological Abstracts, and Web of Science databases) and grey literature (using Google Scholar) to identify studies on the mental health effects of the six pandemics in the last 20 years (2000-2020). We identified a total of 32 articles; all of them pertained to COVID-19 except for one about Ebola. We found there was an increase in depression, anxiety, stress, and other mental health issues among non-health essential workers. They were more worried about passing the infection on to their loved ones and often did not have adequate training, supply of personal protective equipment, and support to cope with the effects. Generally, women, people having lower education, and younger people were more likely to be affected by a pandemic. Exploring occupation-specific coping strategies of those whose mental health was affected during a pandemic using more robust methodologies such as longitudinal studies and in-depth qualitative exploration would help facilitate appropriate responses for their recovery.
Keyphrases
- mental health
- coronavirus disease
- sars cov
- healthcare
- mental illness
- public health
- depressive symptoms
- systematic review
- primary care
- type diabetes
- health information
- adipose tissue
- big data
- case control
- deep learning
- risk assessment
- insulin resistance
- social media
- artificial intelligence
- quality improvement
- pregnancy outcomes
- medical students
- pregnant women