| Literature DB >> 35912469 |
Wei Li1,2, Ali Nawaz Khan1.
Abstract
While past research has focused on the benefits of social media during pandemics, this study emphasizes the possible negative effects of social media use among healthcare professionals. It has been stated that healthcare professionals are exposed to COVID-19 and its impacts on the mental health of these workers. Even though recognizing the importance of healthcare professionals during the pandemic, the impacts of COVID-19 on the mental health of healthcare professionals have been rarely considered for investigation by researchers. By applying differential susceptibility to the media effects model (DSMM), the current article investigated the effect of COVID-19 information overload (CIO) on psychological and mental well-being and underline mechanisms. Time-wave technique was applied to collect the data. This study tested moderated mediation model by collecting data from 314 healthcare professionals. The findings stated that COVID-19 information overload impacted COVID-19 fatalism and COVID-19 exhaustion directly. Likewise, COVID-19 fatalism mediated the association between CIO and COVID-19 exhaustion. Moreover, the COVID-19 stressor moderated this mediating relationship. This study proposes several practical recommendations for healthcare professionals, social media platform providers, health authorities, organizations, and institutions on how to use social media effectively and sustainably during the global COVID-19 epidemic.Entities:
Keywords: COVID-19 exhaustion; COVID-19 information overload; COVID-19 stressor; delivery of health care; fatalism; healthcare professionals; mental health; pandemics; social media
Mesh:
Year: 2022 PMID: 35912469 PMCID: PMC9340904 DOI: 10.1177/00469580221109677
Source DB: PubMed Journal: Inquiry ISSN: 0046-9580 Impact factor: 2.099
Details About Demographic Variables and Social Media Use.
| Variables | N | Percentage |
|---|---|---|
|
| ||
| Male | 110 | 65.0 |
| Female | 204 | 35.0 |
|
| ||
| Up to 20 | 17 | 05.4 |
| Between 21 and 30 | 116 | 36.9 |
| Between 31 and 40 | 96 | 30.6 |
| Between 41 and 50 | 65 | 20.7 |
| Above 50 | 20 | 06.4 |
|
| ||
| Medical doctors | 145 | 46.2 |
| Nurses | 106 | 33.7 |
| Other staff | 63 | 20.1 |
|
| ||
| Upto-1 year | 31 | 09.9 |
| 1-3 years | 51 | 16.2 |
| 3-5 years | 61 | 19.4 |
| 5-7 years | 171 | 54.5 |
|
| ||
| Rarely | 08 | 02.6 |
| Occasionally | 19 | 06.1 |
| Sometimes | 18 | 05.7 |
| Frequently | 110 | 35.0 |
| Almost all of the time | 159 | 50.6 |
Factors Loadings, Alpha, Composite Reliability and AVE.
| Constructs | Items | Loadings | CR | Cronbach’s alpha | AVE |
|---|---|---|---|---|---|
| COVID-19 Fatalism (FAT) | FAT1 | 0.674 | 0.83 | .83 | 0.50 |
| FAT2 | 0.675 | ||||
| FAT3 | 0.730 | ||||
| FAT4 | 0.644 | ||||
| FAT5 | 0.789 | ||||
| COVID-19 Exhaustion (EXH) | EXH1 | 0.747 | 0.80 | .81 | 0.51 |
| EXH2 | 0.666 | ||||
| EXH3 | 0.708 | ||||
| EXH4 | 0.724 | ||||
| COVID-19 Information Overload on Social Media (CIO) | CIO1 | 0.726 | 0.79 | .80 | 0.55 |
| CIO2 | 0.731 | ||||
| CIO3 | 0.773 |
Note. All factor loadings are significant at the P < .001 level.
CR = composite reliability; AVE = average variance extracted.
Descriptive Statistics, Square Root of AVE and Correlation Matrix.
| Constructs | Mean | SD | 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|---|
| 1.COVID-19 stressor | 4.17 | 1.74 |
| |||
| 2.COVID-19 information overload on social media | 3.55 | 1.16 | 0.10 |
| ||
| 3.COVID-19 fatalism | 3.35 | 0.84 | 0.19 | 0.27 |
| |
| 4.COVID-19 exhaustion | 3.47 | 0.79 | 0.11 | 0.35 | 0.34 |
|
Note. (1) Correlation is significant at the ***P < .001, **P < .01, *P < .05; (2) Square roots of AVE for every constructs is shown in parentheses, (3) n = 314.
Figure 1.Result of the path analysis.
***P < .001. **P < .01.
Figure 2.Interaction effects of COVID-19 stressor and COVID-19 information overload on COVID-19 fatalism.