It’s all about beliefs: Believing emotions are uncontrollable is linked to symptoms of anxiety and depression through cognitive reappraisal and expressive suppression

Deplancke, Carla; Somerville, Matthew P.; Harrison, Amy; Vuillier, Laura

doi:10.1007/s12144-022-03252-2

Cited by 16 publications

(10 citation statements)

References 39 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These classifications are determined based on specific score ranges. In the case of depression, the five levels are categorized as normal (0-9), mild (10)(11)(12)(13), moderate (14)(15)(16)(17)(18)(19)(20), severe (21)(22)(23)(24)(25)(26)(27), and extremely severe (≥28). Similarly, for anxiety, the levels are categorized as normal (0-7), mild (8)(9), moderate (10)(11)(12)(13)(14), severe (15)(16)(17)(18)(19), and extremely severe (≥20).…”

Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tuan,

Trang,

et al. 2024

J. Hum. Earth Future

View full text Add to dashboard Cite

Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model’s predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual’s mental health among recovered COVID-19 patients (accuracy ≥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection. Doi: 10.28991/HEF-2024-05-01-01 Full Text: PDF

show abstract

Section: Classified Output Of Depression Anxiety and Stressmentioning

confidence: 99%

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tuan,

Trang,

et al. 2024

J. Hum. Earth Future

View full text Add to dashboard Cite

show abstract

“…In addition, future empirical work could clarify potential mediators and moderators in the relationships between emotion perspectives and clinical outcomes. Initial work in this domain has clarified that differences in emotion regulation, such as the use of cognitive reappraisal or expressive suppression, mediated the relationship between emotion perspectives and mental health, including depressive, anxiety, and eating disorder symptoms (Deplancke et al, 2022 ; Vuillier et al, 2021 ). Additionally, work by Schroder and others describe how anxiety mindsets related to clinical symptoms, such as substance use, depression, and non-suicidal self-injury, and buffered the effect of stressful life events on certain symptoms (Schroder et al, 2017 ).…”

Section: Overview Of Papers and Research Themes Included In The Speci...mentioning

confidence: 99%

Lay perspectives on emotion: past, present, and future research directions

Kneeland

Kisley

2023

Motiv Emot

View full text Add to dashboard Cite

Empirical research has demonstrated that individuals vary widely in how they view their emotions. We call the viewpoints that individuals have towards their emotions emotion perspectives. While many subdisciplines of psychology, such as social psychology and clinical psychology, have studied this topic, research thus far can be siloed, despite overlap in terms and constructs. The goal of the current special issue and this introduction is to describe the state of research on emotion perspectives, highlight common themes in streams of emotion perspective research, and present future directions for investigation. The first portion of this introduction to the special issue provides a basic review of emotion perspective research, spotlighting topics such as emotion beliefs, emotion mindsets, lay theories of emotion, and attitudes toward emotion. The second portion of the introduction presents themes that cut across papers in the special issue, with a discussion of future research directions throughout. The goal of this introduction and special issue is to serve as a guide for greater integration in emotion perspective research and to provide a roadmap for emotion perspective research moving forward.

show abstract

“…Consistent with the notion, Tamir et al (2007) found that college students with entity emotion beliefs experienced fewer positive emotions and more negative emotions during the initial period of the transition to college. Moreover, prior studies showed that emotion malleability beliefs were related to psychological symptoms such as depression (De Castella et al, 2018; Deplancke et al, 2022), anxiety (De Castella et al, 2013; Kneeland et al, 2016b; Schroder et al, 2019), and fatigue (Sydenham et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Relation of emotion malleability beliefs to subjective well‐being: A short‐term longitudinal study

Zhang

et al. 2023

Applied Psych Health & Well

View full text Add to dashboard Cite

Prior research has shown that emotion malleability beliefs are positively related to subjective well‐being, but less is known about the longitudinal relationship between both variables. The present study used a two‐wave longitudinal design to examine the temporal directionality of the relationship in a sample of Chinese adults. Using cross‐lagged models, we found that emotion malleability beliefs predicted all three dimensions of subjective well‐being (i.e. positive affect, life satisfaction, and negative affect) 2 months later. However, we did not detect any reverse or reciprocal effect between emotion malleability beliefs and subjective well‐being. In addition, emotion malleability beliefs still predicted life satisfaction and positive affect after controlling for the effect of the cognitive or emotional component of subjective well‐being. Our study provided primary evidence for the temporal directionality of the association between emotion malleability beliefs and subjective well‐being. Implications and suggestions for future research were discussed.

show abstract

It’s all about beliefs: Believing emotions are uncontrollable is linked to symptoms of anxiety and depression through cognitive reappraisal and expressive suppression

Cited by 16 publications

References 39 publications

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Lay perspectives on emotion: past, present, and future research directions

Relation of emotion malleability beliefs to subjective well‐being: A short‐term longitudinal study

Contact Info

Product

Resources

About