In Social work
The crisis created by the spread of COVID-19 brought increasing needs and referrals to social welfare services in many countries. However, at the same time, social services suffered from staff cutbacks and service closures, resulting in significant workload increases to address the hardships associated with the pandemic. This article investigates the impact of the COVID-19 pandemic on Israeli social workers' well-being, using a mixed-methods design with a sample of 2,542 licensed social workers. Findings show that over 70 percent of social workers suffered from at least one health problem related to their work. Path analysis findings indicated that social workers who experienced greater service restrictions reported a greater decrease in job satisfaction and experienced higher levels of stress and work-related problems. Machine learning emotion-detection analysis revealed that the pandemic affected their lives, causing feelings of fear, frustration, and sadness. This article demonstrates how social workers whose work was characterized by greater service restrictions were less satisfied with their jobs, more stressed, and experienced greater job-related health problems, and concludes with a discussion of the implications for social work practice in times of crisis.
Schwartz Tayri Talia Meital
2022-Nov-21
COVID-19 pandemic, machine learning emotion detection, moral distress, moral injury, social work services
