scholarly journals Exploring symptoms of somatization in chronic widespread pain: latent class analysis and the role of personality

2017 ◽  
Vol Volume 10 ◽  
pp. 1733-1740 ◽  
Author(s):  
Andrea Burri ◽  
Peter Hilpert ◽  
Peter McNair ◽  
Frances Williams
Author(s):  
Katarina Aili ◽  
Paul Campbell ◽  
Zoe A Michaleff ◽  
Victoria Y. Strauss ◽  
Kelvin Jordan ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.


Sociologie ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 117-147
Author(s):  
Gijs Custers ◽  
Godfried Engbersen

Abstract Studies by Savage et al. (2013) and Vrooman, Gijsberts and Boelhouwer (2014) introduce new class typologies that combine Bourdieu’s work with latent class analysis. This paper identifies this new research approach as Bourdieusian latent class analysis. We discuss the role of these studies within the social class debate and we review the merits and limitations of this approach. In addition, we show how the class structure of Rotterdam can be empirically established by studying the distribution of economic, social and cultural capital. We use the Neighbourhood Profile data (N = 14,040; 71 neighbourhoods) to develop a class typology that includes eight social groups. This class typology complements conventional indicators of neighbourhood socioeconomic status and can be used to study ‘social mix’ and gentrification.


2018 ◽  
Vol 36 (15_suppl) ◽  
pp. e22161-e22161
Author(s):  
Alessandro Rossi ◽  
Maria Marconi ◽  
Stefania Mannarini ◽  
India Minelli ◽  
Chiara Rossini ◽  
...  

Foods ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Ching-Hua Yeh ◽  
Monika Hartmann ◽  
Nina Langen

This paper presents empirical findings from a combination of two elicitation techniques—discrete choice experiment (DCE) and best–worst scaling (BWS)—to provide information about the role of consumers’ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participants’ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumers’ attitude and trust significantly explain class membership and therefore, consumers’ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy.


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