Explanation of somatic symptoms by mental health and personality traits: application of Bayesian regularized quantile regression in a large population study

Mostafaei, Shayan; Kabir, Kourosh; Kazemnejad, Anoshirvan; Feizi, Awat; Mansourian, Marjan; Keshteli, Ammar Hassanzadeh; Afshar, Hamid; Arzaghi, Seyed Masoud; Dehkordi, Saeid Rasekhi; Adibi, Peyman; Ghadirian, Fataneh

doi:10.1186/s12888-019-2189-1

Cited by 38 publications

(37 citation statements)

References 37 publications

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“…Neuroticism positively correlated with stress, anxiety and depression. These results were also found by Vujičić and Ranđelović (2017) and Mostafaei et al (2019), and similar results were reported by other studies (Brailovskaia & Margraf, 2016;Ervasti et al, 2019;Gramstad et al, 2013;Milić et al, 2019;Mousavi & Essazadegan, 2016;Pereira-Morales et al, 2019;Seto et al, 2019;Sørengaard et al, 2019). In contrast, (A) negatively correlated with stress, anxiety and depression.…”

Section: Resultssupporting

confidence: 90%

“…In contrast, (A) negatively correlated with stress, anxiety and depression. These correlations were reported by https: //doi.org/10.15405/epsbs.2020.05.17 Corresponding Author: Ana Moreno Selection and peer-review under 157 Mostafaei et al (2019). In the study by Vujičić and Ranđelović (2017) it was found the same results but it was not found the stress correlation.…”

Section: Resultsmentioning

confidence: 86%

“…Mental health is the result of a complex interaction of social, psychological and biological factors (e.g., personality) (Ogboghodo, Osadiaye, & Omosun-Fadal, 2018). Personality has an impact on mental health that we cannot ignore and has been studied in several studies (Brailovskaia & Margraf, 2016;Ervasti, Kallio, Määttänen, Mäntyjärvi, & Jokela, 2019;Gramstad, Gjestad, & Haver, 2013;Lyons, Evans, & Helle, 2019;MacIntyre et al, 2019;Milić, Škrlec, Milić Vranješ, Podgornjak, & Heffer, 2019;Mostafaei et al, 2019;Pereira-Morales, Adan, & Forero, 2019;Seto et al, 2019;Sørengaard et al, 2019). There may be personality traits that protect individuals against mental distress in the face of adversity (Lyons et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Personality And Mental Health: The Moderator Role Of Emotional Intelligence

Lira¹,

Gil²,

Gil³

et al. 2020

European Proceedings of Social and Behavioural Sciences

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personality And Mental Health: The Moderator Role Of Emotional Intelligence

Lira¹,

Gil²,

Gil³

et al. 2020

European Proceedings of Social and Behavioural Sciences

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“…Quantile regression by R software ( Mostafaei et al, 2019 ) was further applied to demonstrate whether the level of PIU was related to the association between PIU and engaged living. The results of quantile regression are shown in Figures 1A – C .…”

Section: Resultsmentioning

confidence: 99%

Heterogeneous Association of Chinese Adolescents’ Engaged Living With Problematic Internet Use: A Mixture Regression Analysis

Zhang

Jiao

et al. 2021

Front. Psychol.

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The present study explored heterogeneity in the association between engaged living (i.e., social integration and absorption) and problematic Internet use (PIU). This study included 641 adolescents from four junior-senior high schools of Guangzhou, China. Besides the standard linear regression analysis, mixture regression analysis was conducted to detect certain subgroups of adolescents, based on their divergent association between engaged living and PIU. Sex, age, and psychological need were further compared among the latent subgroups. The results showed that a mixture regression model could account for more variance of PIU than a traditional linear regression model, and identified three subgroups based on their class-specific regression of PIU to engaged living. For the High-PIU class, lower social integration and higher absorption were associated with increased PIU; for the Medium-PIU class, only high social integration was linked with the increase of PIU. For the Low-PIU class, no relation between engaged living and PIU were found. Additionally, being male or having a lower level of satisfied psychological needs increased the link between engaged living and PIU. The results indicated a heterogeneous relationship between engaged living and PIU among adolescents, and prevention or intervention programs should be tailored specifically to subgroups with moderate or high levels of PIU and to those with lower levels of psychological needs’ satisfaction, as identified by the mixture regression model.

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“…The participants were majorly involved in industries such as telecommunications, petroleum, and aeronautics. Stress analysis among banking employees was done across the countries from the Middle East to Africa [ 19 ]. It is also understood that job status is one of the prime reasons for getting stress.…”

Section: Related Workmentioning

confidence: 99%

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

Vincent

Mahendran

Nebhen

et al. 2021

Computational Intelligence and Neuroscience

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Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals’ lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime. In simple terms, it is a mood disturbance that can persist for an individual for more than a few weeks to months. In MDD, in most cases, the individuals do not consult a professional, and even if being consulted, the results are not significant as the individuals find it challenging to identify whether they are depressed or not. Depression, most of the time, cooccurs with anxiety and leads to suicide in few cases, among the employees, who are about to handle the pressure at work and home and mostly unnoticing such problems. This is why this work aims to analyze the IT employees who are mostly working with targets. The artificial neural network, which is modeled loosely like the brain, has proved in recent days that it can perform better than most of the classification algorithms. This study has implemented the multilayered neural perceptron and experimented with the backpropagation technique over the data samples collected from IT professionals. This study aims to develop a model that can classify depressed individuals from those who are not depressed effectively with the data collected from them manually and through sensors. The results show that deep-MLP with backpropagation outperforms other machine learning-based models for effective classification.

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Explanation of somatic symptoms by mental health and personality traits: application of Bayesian regularized quantile regression in a large population study

Cited by 38 publications

References 37 publications

Personality And Mental Health: The Moderator Role Of Emotional Intelligence

Personality And Mental Health: The Moderator Role Of Emotional Intelligence

Heterogeneous Association of Chinese Adolescents’ Engaged Living With Problematic Internet Use: A Mixture Regression Analysis

Performance Assessment of Certain Machine Learning Models for Predicting the Major Depressive Disorder among IT Professionals during Pandemic times

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