| Literature DB >> 34910779 |
Marcela Matos1, Kirsten McEwan2, Martin Kanovský3, Júlia Halamová4, Stanley R Steindl5, Nuno Ferreira6, Mariana Linharelhos1, Daniel Rijo1, Kenichi Asano7, Sara P Vilas8, Margarita G Márquez8, Sónia Gregório1,8, Gonzalo Brito-Pons9, Paola Lucena-Santos1, Margareth da Silva Oliveira10, Erika Leonardo de Souza11, Lorena Llobenes12, Natali Gumiy12, Maria Ileana Costa12, Noor Habib13, Reham Hakem13, Hussain Khrad13, Ahmad Alzahrani13, Simone Cheli14, Nicola Petrocchi15, Elli Tholouli16, Philia Issari16, Gregoris Simos17, Vibeke Lunding-Gregersen18, Ask Elklit19, Russell Kolts20, Allison C Kelly21, Catherine Bortolon22,23, Pascal Delamillieure24,25, Marine Paucsik22, Julia E Wahl26,27, Mariusz Zieba27, Mateusz Zatorski27, Tomasz Komendziński28,29, Shuge Zhang30, Jaskaran Basran2, Antonios Kagialis6, James Kirby5, Paul Gilbert2.
Abstract
BACKGROUND: Historically social connection has been an important way through which humans have coped with large-scale threatening events. In the context of the COVID-19 pandemic, lockdowns have deprived people of major sources of social support and coping, with others representing threats. Hence, a major stressor during the pandemic has been a sense of social disconnection and loneliness. This study explores how people's experience of compassion and feeling socially safe and connected, in contrast to feeling socially disconnected, lonely and fearful of compassion, effects the impact of perceived threat of COVID-19 on post-traumatic growth and post-traumatic stress.Entities:
Mesh:
Year: 2021 PMID: 34910779 PMCID: PMC8673633 DOI: 10.1371/journal.pone.0261384
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Likelihood-ratio tests and information criteria for the Post Traumatic Growth (PTG) models.
| Model | Deviance | χ2 (df) | p-value | AIC | BIC | dist | variance |
|---|---|---|---|---|---|---|---|
| m1 | 37219 | - | - | 37271 | 37434 | normal | homoscedastic |
| m2 | 37032 | 187 (5) | < .001 | 37127 | 37425 | normal | heteroscedastic |
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Note. χ2 = chi-square. df = degrees of freedom. AIC = Akaike information Criterion. BIC = Bayes-Schwarz Information Criterion. dist = distribution., SEP = Skew Power Exponential. Best model is displayed in bold.
The coefficients of best fitting Post Traumatic Growth (PTG) model—the Skew Exponential Power model.
| Predictor | β (SE) | p-value | Σ (SE) | ν (SE) | τ (SE) |
|---|---|---|---|---|---|
| Intercept |
| < .001 |
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| PTCSFear |
| < .001 |
| - | - |
| SocialConnection |
| < .001 |
| - | - |
| SocialDisconnection | -0.26 (0.78) | .737 |
| - | - |
| PTCSFear:SocialConnection |
| .023 |
| - | - |
| PTCSFear:SocialDisconnection | -0.11 (0.09) | .194 |
| - | - |
Note. β = beta coefficient. SE = standard error. Σ (sigma) = variance. ν (nu) = skewness parameter. τ (tau) = kurtosis parameter. Significant effects are displayed in bold.
Likelihood-ratio tests and information criteria for the Post-Traumatic Stress (IESR) models.
| Model | Deviance | χ2 (df) | p-value | AIC | BIC | dist | variance |
|---|---|---|---|---|---|---|---|
| n1 | 35118 | - | - | 35271 | 35434 | normal | homoscedastic |
| n2 | 35032 | 89 (5) | < .001 | 35132 | 35412 | normal | heteroscedastic |
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Note. χ2 = chi-square. df = degrees of freedom. AIC = Akaike information Criterion. BIC = Bayes-Schwarz Information Criterion. dist = distribution., SEP = Skew Power Exponential. Best model is displayed in bold.
The coefficients of best fitting Post-Traumatic Stress (IESR) model—the Skew Exponential Power model.
| Predictor | β (SE) | p-value | Σ (SE) | ν (SE) | τ (SE) |
|---|---|---|---|---|---|
| Intercept |
| < .001 |
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| PTCSFear |
| < .001 |
| - | - |
| SocialConnection |
| < .001 | 0.01 (0.01) | - | - |
| SocialDisconnection |
| .003 |
| - | - |
| PTCSFear:SocialConnection | 0.36 (0.22) | .099 | -0.01 (0.01) | - | - |
| PTCSFear:SocialDisconnection |
| < .001 |
| - | - |
Note. β = beta coefficient. SE = standard error. Σ (sigma) = variance. ν (nu) = skewness parameter. τ (tau) = kurtosis parameter. Significant effects are displayed in bold.