Article 2024

Latent profile analysis of emotional clarity and acceptance in school-to-work transition students: association with depression and life satisfaction

Current Psychology
Journal · Vol. 43 · Issue 11 · pp. 9889-9898
Abstract

This study identified school-to-work transition student profiles based on emotional clarity and acceptance levels and examined how these profiles differed according to depression and life satisfaction levels. A latent profile analysis of 375 Korean college students identified four groups: (a) medium clarity and low acceptance (MCLA), (b) low clarity and low acceptance (LCLA), (c) high clarity and high acceptance (HCHA), and (d) medium clarity and medium acceptance (MCMA). Of these, the HCHA group had significantly lower levels of depression and higher levels of life satisfaction than the MCLA, LCLA, and MCMA groups. These results indicate that students not only require a clear understanding of their emotions but must also accept those emotions to prevent depression and experience life satisfaction in transition periods. The paper ends by discussing the study’s limitations and implications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

Keywords

Author Keywords

depression life satisfaction Emotional acceptance Emotional clarity School-to-work transition student

Index Keywords

Author Affiliations
Korea University, Seoul, South Korea
Funding & Acknowledgements
Ministry of Education, MOE
This study was funded by Ministry of Education of the Republic of Korea; National Research Foundation of Korea (Grant/Award Number: NRF-2020S1A5A2A01043871).
National Research Foundation of Korea, NRF
Grant: NRF-2020S1A5A2A01043871
This study was funded by Ministry of Education of the Republic of Korea; National Research Foundation of Korea (Grant/Award Number: NRF-2020S1A5A2A01043871).
National Research Foundation of Korea, NRF
This study was funded by Ministry of Education of the Republic of Korea; National Research Foundation of Korea (Grant/Award Number: NRF-2020S1A5A2A01043871).
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