Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students DOI Creative Commons

Jinqi Ding,

Yue Wu,

Hanxiaoran Li

и другие.

Psychology Research and Behavior Management, Год журнала: 2024, Номер Volume 17, С. 3625 - 3636

Опубликована: Окт. 1, 2024

Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed investigate whether the severity of depressive symptoms in college students affects multivariate anhedonia, smartphone addiction, mediating factors.

Язык: Английский

Network Analysis of Depressive Symptoms in Chinese Sexual Minority Women During the COVID-19 Pandemic: An Intra-Group Perspective DOI
Rui Li, Congrong Shi,

Wanyi Yang

и другие.

Journal of Homosexuality, Год журнала: 2024, Номер unknown, С. 1 - 17

Опубликована: Июнь 4, 2024

The prevalence of depression among sexual minority women is a significant concern, yet no prior research has conducted network analysis depressive symptoms in this population. This the first study to address gap by examining structure Chinese during COVID-19 pandemic, considering both orientation and gender expression as part an intra-group perspective. 1420 completed Center for Epidemiologic Studies Depressive Symptoms (CES-D). Network was employed calculate edge centrality measures, structures lesbians bisexual were compared based on femme, androgyny, butch expression. revealed that core are "Felt depressed," "Fatigue," "Sad," "Failure." Although differences found global strength between different orientations expressions, there symptoms. suggests unique associations social historical contexts emphasizes importance these when providing targeted mental health interventions.

Язык: Английский

Процитировано

0

Pandemic scars: long-term impact of COVID-19 on work stress among healthcare workers in China DOI
Hong Qian,

Sihan Lin,

Lidan Zhang

и другие.

Journal of Health Organization and Management, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Purpose This study mainly focused on the long-term effect of different risk exposure levels and prior anti-epidemic experience healthcare workers in mitigating COVID-19 their work stress post-COVID era. Design/methodology/approach The sample included 359 physicians, 619 nurses, 229 technicians 212 administrators, for a total 1,419 working Lanzhou area during investigation. Data were analyzed by multivariate regression models. Findings Our findings indicated that interaction between pandemic mitigation high-risk significantly affected era increasing ( p < 0.001) reducing rest time 0.001). Healthcare may have experienced worse outcomes long term if they had higher more fighting epidemics. Furthermore, poor mental health with SARS further amplified these adverse effects. However, surprisingly, we did not observe any or > 0.1). Research limitations/implications impact left long-lasting effects Health professionals (HPs), particularly those high Risk (RE) experience. Poor Mental (MH) previous similar outbreaks (such as SARS) are factors should be considered. Support programs must designed promoted to help HPs respond improve performance. Originality/value presents compelling evidence will detrimental workers.

Язык: Английский

Процитировано

0

Using Network Analysis to Subgroup Risk Factors for Depressive Symptoms in College Students DOI Creative Commons

Jinqi Ding,

Yue Wu,

Hanxiaoran Li

и другие.

Psychology Research and Behavior Management, Год журнала: 2024, Номер Volume 17, С. 3625 - 3636

Опубликована: Окт. 1, 2024

Network modeling has been suggested as an effective method to explore intricate relationships among antecedents, mediators, and symptoms. In this study, we aimed investigate whether the severity of depressive symptoms in college students affects multivariate anhedonia, smartphone addiction, mediating factors.

Язык: Английский

Процитировано

0