Back

Research on the mental health status of frontline medical staff during the normalization of the COVID-19 pandemic

sun, n.; Li, L.; Xu, J.; Zhou, s.; Li, H.; Yang, s.; Fan, C.

2023-01-09 health policy
10.1101/2023.01.09.23284361 medRxiv
Show abstract

ObjectiveThis study aimed to investigate the relationship between personality characteristics and psychological health of hospitals frontline medical staff and provide a basis and reference for targeted psychological health education for frontline medical staff and for the staff of related departments to formulate relevant policies. MethodsThe self-evaluation scale of symptoms (SCL-90) was used to investigate the mental health status of 150 first-line medical staff in Zhejiang Province in response to the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. ResultsThe average scores of SCL-90 and somatization, obsessive-compulsive, depression, anxiety, hostility, terror, and psychotic factors were significantly higher than those of the normal sample in the first-aid medical staff of Aihu Hubei. The degree of influence on the mental health status of the frontline medical staff in service in Hubei is as follows, from high to low: the degree of suspicion that they may have been infected when new coronavirus pneumonia-related symptoms occur, the degree of fear of being infected and thus bring the infection to their families, and whether they have received a medical check-up recently, as well as a high level of education (both P<0.05). ConclusionThe psychological health level of the frontline medical staff is lower than the national norm. In the context of the increasing number of confirmed cases and the new type of coronavirus pneumonia in the absence of any specific curative treatments, the frontline medical staff is under great psychological pressure. It is necessary to institute targeted mental health promotion to relieve the psychological pressure endured by the frontline medical staff, promote their physical and mental health, and better respond to the pandemic in China.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.