Psychological flexibility profiles, college adjustment, and subjective well-being among college students in China: A latent profile analysis

Bi, Dandan; Li, Xinying

doi:10.1016/j.jcbs.2021.01.008

Cited by 20 publications

(25 citation statements)

References 44 publications

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“…The three-classification model is consistent with the existing research results. 35 , 36 Specifically, the college students in the self-contradiction group (19.2%) were characterized by a lack of balance in PF and large fluctuations in different dimensions. Although the number of this group is relatively small, their mental health problems should be paid high attention to.…”

Section: Discussionmentioning

confidence: 99%

“…Model 3 was qualitatively different from the trajectories that emerged in model 4, so we kept model 3, which is consistent with previous studies. 35,36 Further analysis revealed that model 4 had a small group. The attribution probability matrix results showed that the average probability of college students in the three categories belonging to each latent profile was greater than 91%.…”

Section: Latent Profile Analysis Of Pf Among College Studentsmentioning

confidence: 99%

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The Relationship Between Psychological Flexibility and Depression, Anxiety and Stress: A Latent Profile Analysis

Wang¹,

Fang²,

Yang³

et al. 2023

PRBM

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Purpose To explore the potential classification of psychological flexibility (PF) among Chinese college students, analyze whether there is group heterogeneity in PF, and discuss the differences in the latent profile of PF in the negative emotions of depression, anxiety, and stress. Methods A total of 1769 college students were investigated using the Psychological Flexibility Questionnaire and Depression-Anxiety-Stress Self-rating Scale, and heterogeneity was tested by latent profile analysis. Results The PF of college students can be divided into three latent profiles: self-contradiction group (19.2%), high PF group (34.1%), and low PF group (46.7%). The scores of depression, anxiety, and stress are significantly different among the groups. Conclusion There is obvious group heterogeneity in the PF of college students, and they can be divided into the self-contradiction group, the high PF group, and the low PF group. The self-contradiction and low PF groups score significantly higher on the negative emotions of depression, anxiety, and stress than the high PF group.

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Section: Discussionmentioning

confidence: 99%

Section: Latent Profile Analysis Of Pf Among College Studentsmentioning

confidence: 99%

The Relationship Between Psychological Flexibility and Depression, Anxiety and Stress: A Latent Profile Analysis

Wang¹,

Fang²,

Yang³

et al. 2023

PRBM

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“…The results showed that college students' resilience was significantly positively correlated with each dimension of attribution style [30]. DandanBi and XinyingLi's research using potential profile analysis showed that college students with different levels of resilience (PF value) had different subjective well-being, and college students with high resilience (HPF) had higher subjective well-being [31].…”

Section: Psychological Resiliencementioning

confidence: 96%

Probe into the Positive Factors of College Students' Subjective Well-being

Bing¹,

Du²,

Li³

2023

BCPEP

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"Subjective well-being" has increasingly become a hot topic of widespread concern. Researchers often use tools such as foreign scales to measure and study college students' subjective well-being. As there are many factors that affect subjective well-being, this essay has a tendentious and comprehensive summary of these research results. It summarizes the results from the following four aspects: individual abilities, psychological factors, environmental factors and demographic factors. As for the specific influence depth, it is still to be further studied. This paper aims to provide a summary of previous studies on college students’ subjective well-being and give some advice about directions in the future to make contributions to improve college students’ subjective well-being. This paper summarizes the positive factors that affect college students' subjective well-being from four aspects: individual ability factors, emotion, environment, and demography. However, the level of the summary is still shallow, and the systematic summary among the factors is not deep enough, which needs to be criticized and corrected and further studied by future generations.

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“…However, there is more heterogeneity across studies in this regard than the results on regular centrality. Some did not obtain high bridge centrality values for "Sad mood" (Kaiser et al, 2021) and different symptoms like "Psychomotor agitation/retardation" (Beard et al, 2016;Bi & Li, 2021;Kaiser et al, 2021), "Worry" (Konac et al, 2021), "Restlessness" (Garabiles et al, 2019;Ren et al, 2021), "Anhedonia" (Bai et al, 2021;Garabiles et al, 2019) were found to have the most connections with the nodes from the opposite community (had high bridge centrality).…”

Section: Identification Of Central Symptomsmentioning

confidence: 98%

“…Multiple studies have tackled this subject regarding anxiety and depression comorbidity, operationalised through the DSM-5 diagnostic criteria for Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD). Symptoms "Sad mood" and "Excessive worry" have been almost ubiquitously identified as the most central (or highly central) in cross-sectional networks (An et al, 2019;Bai et al, 2021;Beard et al, 2016;Bi & Li, 2021;Cramer et al, 2010;Fried et al, 2016;Garabiles et al, 2019;Konac et al, 2021;McElroy et al, 2018;Peel et al, 2021;Ren et al, 2021;Tundo et al, 2021). Symptoms "Anhedonia", "Worthlessness", and "Fatiguability" were also identified as highly central, although with greater variability across studies.…”

Section: Identification Of Central Symptomsmentioning

confidence: 99%

The Dynamic Interplay of Anxious and Depressive Symptoms in a Sample of Undergraduate Students

Marian

Costantini

Macsinga

et al. 2022

J Psychopathol Behav Assess

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The network perspective on psychopathology suggests that mental disorders can be regarded as networks of elements that influence each other. In this study, we used network analysis to explore the temporal interactions of anxiety and depression symptoms at the level of day-to-day experiences and find potential explanatory pathways for their comorbidity. We collected intensive longitudinal data from a sample of undergraduate students and fitted a Multilevel Vector Autoregressive model on GAD and MDD DSM-5 symptoms. “Sad mood” and “Concentration difficulties” were responsible for the most connections between anxiety and depression symptoms and were also among the most central symptoms. It is possible that anxio-depressive comorbidity can be explained by the presence of “Sad mood” and “Concentration difficulties” and targeting these two symptoms in therapy can lead to beneficial effects in comorbid cases.

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Psychological flexibility profiles, college adjustment, and subjective well-being among college students in China: A latent profile analysis

Cited by 20 publications

References 44 publications

The Relationship Between Psychological Flexibility and Depression, Anxiety and Stress: A Latent Profile Analysis

The Relationship Between Psychological Flexibility and Depression, Anxiety and Stress: A Latent Profile Analysis

Probe into the Positive Factors of College Students' Subjective Well-being

The Dynamic Interplay of Anxious and Depressive Symptoms in a Sample of Undergraduate Students

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