Corrigendum: Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy

Gabbiadini, Alessandro; Baldissarri, Cristina; Durante, Federica; Valtorta, Roberta Rosa; De Rosa, Maria; Gallucci, Marcello

doi:10.3389/fpsyg.2021.701130

CORRECTION article

Front. Psychol., 26 May 2021

Sec. Personality and Social Psychology

Volume 12 - 2021 | https://doi.org/10.3389/fpsyg.2021.701130

Corrigendum: Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy

1. University of Milano-Bicocca, Department of Psychology – Mind and Behavior Technological Center, Milan, Italy
2. University of Milano-Bicocca, Department of Psychology, Milan, Italy

This article is a correction to:

Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy
1. Read original article

A Corrigendum on Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy by Gabbiadini, A., Baldissarri, C., Durante, F., Valtorta, R. R., De Rosa, M., and Gallucci, M. (2020). Front. Psychol. 11:554678. doi: 10.3389/fpsyg.2020.554678

Due to a clerical error, the values for the amount of technology use for business/school motives before the lockdown were reported incorrectly in Table 2 and Table 3. The amended Table 2 and Table 3 appears below.

Table 2

		α	M	SD	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	Age	–	31.26	13.19
2	Gender	–	–	–	−0.163^**
3	Days of isolation	–	14.15	7.18	−0.206^**	0.075
4	Number of exits	–	2.36	1.63	0.358^**	−0.089	−0.550^**
5	Number of persons living with	–	2.96	1.30	−0.283^**	0.027	0.075	−0.118^*
6	House sqm	–	123.09	77.09	−0.106^*	0.002	0.085	−0.055	0.357^**
7	Past technology use	–	1.75	0.55	0.168^**	−0.149^**	−0.013	0.012	−0.074	−0.030
8	Amount of technology use	–	2.42	0.70	−0.196^**	−0.021	0.037	−0.147^**	−0.075	0.002	0.474^**
9	Past tech use for business/school	–	1.92	1.18	0.353^**	−0.165^**	−0.194^**	0.233^**	−0.158^**	−0.113^*	0.183^**	0.056
10	Frequency tech use for business/school	–	2.40	1.35	0.181^**	−0.100^*	−0.027	0.096^*	−0.049	−0.002	0.116^*	0.099^*	0.562^**
11	Social support	0.89	5.53	0.96	0.115^*	0.077	−0.039	−0.003	0.013	0.022	0.177^**	0.162^**	0.038	−0.014
12	Loneliness	0.93	2.80	1.08	−0.249^**	0.052	0.025	−0.085	0.034	0.006	−0.164^**	−0.003	−0.078	−0.022	−0.507^**
13	State boredom	0.95	3.79	1.16	−0.367^**	0.198^**	0.114^*	−0.145^**	0.037	−0.011	−0.136^**	0.078	−0.145^**	−0.110^*	−0.245^**	0.617^**
14	State irritability	0.90	3.50	1.31	−0.399^**	0.242^**	0.117^*	−0.140^**	0.164^**	0.030	−0.129^**	0.089	−0.168^**	−0.059	−0.250^**	0.503^**	0.685^**
15	State anger	0.90	2.65	1.23	−0.330^**	0.196^**	0.094^*	−0.078	0.074	0.030	−0.072	0.091^*	−0.102^*	−0.059	−0.248^**	0.502^**	0.657^**	0.733^**
16	State anxiety	0.84	4.48	1.23	−0.195^**	0.301^**	−0.024	−0.032	0.075	−0.023	−0.114^*	0.041	−0.090	−0.063	−0.080	0.349^**	0.571^**	0.567^**	0.565^**
17	Belongingness	0.80	4.53	1.01	0.187^**	0.128^**	−0.003	0.004	0.019	0.015	0.091	0.125^**	0.056	0.029	0.428^**	−0.311^**	−0.230^**	−0.223^**	−0.213^**	-0.039

Descriptive statistics and correlations among variables.

Gender was coded 1 = males and 2 = females. N = 463;

p < 0.05;

p < 0.01.

Table 3

Predictor	Dependent variable	Model statistics	B	*SE B*	β	95%CI		p
						LL	UL
Age	Social support	R² = 0.062, F_{(9, 453)} = 3.31, p < 0.001	0.010	0.004	0.135	0.002	0.017	=0.011
Gender			0.267	0.103	0.121	0.064	0.469	=0.010
Past technology use			0.310	0.082	0.179	0.150	0.471	<0.001
Age	Loneliness	R² = 0.081, F_{(9, 453)} = 4.44, p < 0.001	−0.020	0.004	−0.246	−0.029	−0.012	<0.001
Past technology use			−0.257	0.091	−0.131	−0.437	−0.078	=0.005
Age	Boredom	R² = 0.166, F_{(9, 453)} = 10.04, p < 0.001	−0.031	0.004	−0.352	−0.040	−0.022	<0.001
Gender			0.349	0.118	0.131	0.108	0.581	=0.003
Age	Anger/irritability	R² = 0.179, F_{(9, 453)} = 10.99, p < 0.001	−0.033	0.004	−0.365	−0.041	−0.024	<0.001
Gender			0.470	0.118	0.174	0.238	0.702	<0.001
Age	Anxiety	R² = 0.124, F_{(9, 453)} = 7.11, p < 0.001	−0.014	0.005	−0.155	−0.024	−0.005	=0.003
Gender			0.777	0.128	0.275	0.525	1.028	<0.001
Age	Belongingness	R² = 0.077, F_{(9, 453)} = 4.203, p < 0.001	0.019	0.004	0.243	0.011	0.027	<0.001
Gender			0.406	0.108	0.174	0.193	0.619	<0.001

Significant results of simple and multiple linear regressions.

N = 463; Predictors: (Constant), Age, Gender, Days of isolation, Number of exits, Number of persons living with, House sqm, Past technology use, Past tech use for business/school, Frequency tech use for business/school. LL, lower limit; UL, upper limit.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Summary

Keywords

COVID-19, social isolation, social support, digital technology, negative affect

Citation

Gabbiadini A, Baldissarri C, Durante F, Valtorta RR, De Rosa M and Gallucci M (2021) Corrigendum: Together Apart: The Mitigating Role of Digital Communication Technologies on Negative Affect During the COVID-19 Outbreak in Italy. Front. Psychol. 12:701130. doi: 10.3389/fpsyg.2021.701130

Received

27 April 2021

Accepted

05 May 2021

Published

26 May 2021

Volume

12 - 2021

Edited and reviewed by

Patrícia Arriaga, University Institute of Lisbon (ISCTE), Portugal

Updates

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*Correspondence: Alessandro Gabbiadini alessandro.gabbiadini@unimib.it

This article was submitted to Personality and Social Psychology, a section of the journal Frontiers in Psychology

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