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Journal Article

Citation

Huang F, Li S, Li D, Yang M, Ding H, Di Y, Zhu T. Int. J. Environ. Res. Public Health 2022; 19(15).

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph19159200

PMID

35954551

PMCID

PMC9367801

Abstract

As suicides incurred by the COVID-19 outbreak keep happening in many countries, researchers have raised concerns that the ongoing pandemic may lead to "a wave of suicides" in society. Suicidal ideation (SI) is a critical factor in conducting suicide intervention and also an important indicator for measuring people's mental health. Therefore, it is vital to identify the influencing factors of suicidal ideation and its psychological mechanism during the outbreak. Based on the terror management theory, in the present study we conducted a social media big data analysis to explore the joint effects of mortality salience (MS), negative emotions (NE), and cultural values on suicidal ideation in 337 regions on the Chinese mainland. The findings showed that (1) mortality salience was a positive predictor of suicidal ideation, with negative emotions acting as a mediator; (2) individualism was a positive moderator in the first half-path of the mediation model; (3) collectivism was a negative moderator in the first half-path of the mediation model. Our findings not only expand the application of the terror management theory in suicide intervention but provide some insights into post-pandemic mental healthcare. Timely efforts are needed to provide psychological interventions and counseling on outbreak-caused negative emotions in society. Compared with people living in collectivism-prevailing regions, those living in individualism-prevailing regions may be more vulnerable to mortality salience and negative emotions and need more social attention.


Language: en

Keywords

Humans; Emotions; Suicidal Ideation; suicidal ideation; *Suicide; individualism; collectivism; terror management theory; Pandemics; negative emotion; *COVID-19/epidemiology; big data analysis

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