Psychological distress among victimized women on probation and parole: A latent class analysis.

There exists uncertainty for clinicians over how the separate subcomponent processes of psychological flexibility, a core construct of the Acceptance and Commitment Therapy model, interact and influence distress experienced. The present study ( N = 567) employed latent class analysis to (a) identify potential classes (i.e., subgroups) of psychological flexibility based on responses on measures of key subcomponent process and (b) examine whether such classes could reliably differentiate levels of self-reported psychological distress and positive and negative emotionality. We found three distinct classes: (a) High Psychological Flexibility, (b) Moderate Psychological Flexibility, and (c) Low Psychological Flexibility. Those in the Low Psychology Flexibility class reported highest levels of psychological distress, whereas those in the High Psychological Flexibility class reported lowest levels of psychological distress. This study provides a clearer view to clinicians of the profile of the broader spectrum of the psychological flexibility model to facilitate change in clients.

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Schizotypal traits in adolescents: Links to family history of psychosis and psychological distress

European Psychiatry ◽

10.1016/j.eurpsy.2012.04.002 ◽

2012 ◽

Vol 28 (4) ◽

pp. 247-253 ◽

Cited By ~ 31

Author(s):

M. Cella ◽

M. Serra ◽

A. Lai ◽

O.J. Mason ◽

D. Sisti ◽

...

Keyword(s):

Psychological Distress ◽

Family History ◽

Latent Class Analysis ◽

Latent Class ◽

General Health Questionnaire ◽

Positive Association ◽

Total Sample ◽

Class Analysis ◽

Item Endorsement ◽

History Of

AbstractObjectiveStudies in the general population report that unusual subjective experiences are relatively common. Such experiences have been conceptualized either as extreme personality traits or as vulnerability markers for psychosis, and often grouped under the expression “schizotypal experiences”. This study investigates the heterogeneity of schizotypal traits using factor and latent class analysis.MethodsOne thousand and thirty-two adolescents were recruited for this study. Schizotypal experiences were assessed with the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE); psychological distress was assessed with the General Health Questionnaire (GHQ). Confirmatory Factorial Analysis (CFA) and Latent Class Analysis (LCA) were performed on the O-LIFE and on the association with the GHQ and demographic variables.ResultsCFA replicated the original 4-factor structure of the O-LIFE. Three latent classes (LC) of schizotypal features were identified: participants in LC1 (26% of the total sample) showed minimal level of item endorsement; LC2 accounted for 52% of the sample and showed overall higher item endorsement compared to LC1, especially for disorganization and positive signs of schizotypy, but not for negative affective items. LC3 (22%) showed an overall higher level of item endorsement across schizotypal dimensions, and positive association with psychological distress and family history of psychosis.DiscussionDifferent latent class of schizotypal features can be empirically defined in adolescent community samples. The most extreme class is defined not only by a profile of higher positive replies to the items, but also by anhedonia, high psychological distress, and family history of psychosis. These findings can inform prevention research in schizophrenia.

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Using Latent Class Analysis to Identify Health Lifestyle Profiles and Their Association with Suicidality among Adolescents in Benin

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18168602 ◽

2021 ◽

Vol 18 (16) ◽

pp. 8602

Author(s):

Fanny Hoogstoel ◽

Lucresse Corine Fassinou ◽

Sékou Samadoulougou ◽

Céline Mahieu ◽

Yves Coppieters ◽

...

Keyword(s):

Psychological Distress ◽

Latent Class Analysis ◽

Parental Support ◽

Latent Class ◽

Physical Violence ◽

Prevalence Ratio ◽

Health Risk Behaviors ◽

Class Analysis ◽

Health Lifestyle ◽

Increased Risk

Youth suicidality is considerably prevalent in low- and middle-income countries, including Benin. Factors such as psychosocial distress, socio-environmental factors, and health risk behaviors are associated with suicidality. However, little is known about how these factors co-occur in these countries. An analysis of these factors taken together would help to identify the profiles most at risk and better target prevention policies. Our study aimed to identify profiles related to these factors and their association with suicidality among adolescents in Benin. Data from the 2016 Global School-Based Student Health Survey were used, and factors related to lifestyle (tobacco and alcohol consumption and physical activity), physical violence, parental support, and psychological distress were studied. Latent class analysis was used to identify the profiles, and a modified Poisson regression with generalized estimating equations, adjusted for sociodemographic characteristics, was performed to assess the association between these profiles and suicidality. The survey results show that globally, 13.8% of the adolescents (n = 2536) aged 11 to 18 had thought about suicide, 15.6% had planned suicide, and 15.6% had attempted suicide. Four profiles were identified: a low-risk group, one with psychological distress problems, a group with violence problems, and one with alcohol, tobacco, and violence problems. The risk of suicidality, in terms of ideation, planning, or attempting, was higher for adolescents in Profiles 2, 3, and 4 than those in Profile 1 (p < 0.05). Adolescents in Profile 2 were particularly affected by this increased risk (prevalence ratio (PR) for ideation = 1.13, 95% CI = 1.03–1.23; PR for planning = 1.12, 95% CI = 1.04–1.22; PR for attempting = 1.09, 95% CI = 1.01–1.17). This study highlights the typical profiles that may be linked with suicidality among adolescents in Benin. A holistic consideration of these factors could help in planning better preventive measures to reduce suicidality among adolescents in Benin.

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Latent Class Analysis of Multiple Health Risk Behaviors among Australian University Students and Associations with Psychological Distress

Nutrients ◽

10.3390/nu13020425 ◽

2021 ◽

Vol 13 (2) ◽

pp. 425 ◽

Cited By ~ 1

Author(s):

Melinda J. Hutchesson ◽

Mitch J. Duncan ◽

Stina Oftedal ◽

Lee M. Ashton ◽

Christopher Oldmeadow ◽

...

Keyword(s):

Psychological Distress ◽

University Students ◽

Health Risk ◽

Health Behaviors ◽

Latent Class Analysis ◽

Risk Behaviors ◽

Latent Class ◽

Health Risk Behaviors ◽

Demographic Characteristics ◽

Class Analysis

University students have high rates of health risk behaviors and psychological distress. This study explores patterns of health behaviors among a sample of Australian university students, and determines whether patterns of health behaviors are associated with psychological distress and demographic characteristics. Cross-sectional data from the University of Newcastle Student Healthy Lifestyle Survey 2019 were analyzed. Fruit and vegetable intake, sugar-sweetened beverage intake, physical activity, sitting time, smoking, alcohol intake, drug use, sleep and psychological distress were assessed. Latent class analysis (LCA) was used to identify patterns of health risk behaviors, and latent class regression to explore associations between psychological distress and demographic characteristics with health behavior classes. Analysis included 1965 students (mean age 25.8 ± 8.6 years, 70.7% female). Three patterns of health behaviors were identified: healthier (48.6%), moderate (40.2%) and unhealthy (11.2%) lifestyle classes. Students in the moderate and unhealthy lifestyle classes had higher odds of moderate (OR 1.43 and 2.37) and high/very high psychological distress risk (OR 2.71 and 11.69). Students in the unhealthy and moderate lifestyle classes had a higher odds of being male, younger, enrolled in transition to university and English language courses, Aboriginal or Torres Strait Islander descent and to report some financial difficulty. Study findings may be used to inform the design of mental health interventions for university students that target key health risk behaviors.

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