Does Gender Difference Exist in Typologies of Intergenerational Relations? Adult Son–Parent and Daughter–Parent Relations in Hong Kong

2022 ◽  
pp. 0192513X2110669
Author(s):  
Chenhong Peng ◽  
Qijin Cheng ◽  
Paul S. F. Yip
Keyword(s):  
Logistic Regression ◽  
Hong Kong ◽  
Gender Difference ◽  
Latent Class Analysis ◽  
Adult Children ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Intergenerational Relations ◽  
Class Analysis ◽  
Elderly Parents

This study examines the typologies of adult son–parent and daughter–parent relations in Hong Kong, a place where East meets West. Data were drawn from a survey of 834 adult children (381 sons and 453 daughters) aged between 18 and 60 with at least one living parent. Latent class analysis identified four types of relations for both son-parent and daughter-parent relations: tight-knit, distant ascending ties, obligatory, and detached. Sons were more likely to engage in obligatory and tight-knit relations with parents, whereas daughters were more likely to engage in distant ascending ties relations. Multinomial logistic regression found that adult children who were young, single, or co-residing with their own child aged above 18 were more likely to have tight-knit relations with their elderly parents. Our findings suggest that although the male-dominated norm remains influential in Hong Kong, daughters are increasingly maintaining close interactions with their parents.

State-Level Firearm Transfer Policy

2017 ◽  
Vol 18 (2) ◽  
pp. 79-97
Author(s):  
David G. Mueller ◽  
Qiang Fu ◽  
Ronald Frandsen ◽  
Jennifer Karberg ◽  
Evan Anderson
Keyword(s):  
Logistic Regression ◽  
Latent Class Analysis ◽  
Analysis Of Variance ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
State Level ◽  
Class Analysis ◽  
Transfer Policy ◽  
Better Than

The aim of the present study was to determine whether latent class analysis (LCA) could obtain a measure of the aggregate firearm transfer law environment. LCA, analysis of variance, and multinomial logistic regression were used to analyze state-level firearm transfer laws. Results indicated that a three-class solution fit the data better than a two- or four-class solution. These classes were associated with the two covariates in patterns consistent with hypotheses. Results suggest that LCA is a useful technique for classifying states based on the restrictiveness of firearm transfer laws. This classification may be useful in intervention and prevention planning.

scholarly journals Physical Activity and Sedentary Activity Patterns Among Children and Adolescents: A Latent Class Analysis Approach

2011 ◽  
Vol 8 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Carrie D. Patnode ◽  
Leslie A. Lytle ◽  
Darin J. Erickson ◽  
John R. Sirard ◽  
Daheia J. Barr-Anderson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Children And Adolescents ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Activity Patterns ◽  
Moderate Activity ◽  
Class Analysis ◽  
Sedentary Behaviors ◽  
Sedentary Activity

Background:While much is known about the overall levels of physical activity and sedentary activity among youth, few studies have attempted to define clusters of such behaviors. The purpose of this study was to identify and describe unique classes of youth based on their participation in a variety of physical activity and sedentary behaviors.Methods:Latent class analysis was used to characterize segments of youth based on patterns of self-reported and accelerometer-measured participation in 12 behaviors. Children and adolescents (N = 720) from 6th-11th grade were included in the analysis. Differences in class membership were examined using multinomial logistic regression.Results:Three distinct classes emerged for boys and girls. Among boys, the 3 classes were characterized as “Active” (42.1%), “Sedentary” (24.9%), and “Low Media/Moderate Activity” (33.0%). For girls, classes were “Active” (18.7%), “Sedentary” (47.6%), and “Low Media/Functional Activity” (33.7%). Significant differences were found between the classes for a number of demographic indicators including the proportion in each class who were classified as overweight or obese.Conclusions:The behavioral profiles of the classes identified in this study can be used to suggest possible audience segments for intervention and to tailor strategies appropriately.

Application of latent class analysis in assessing the mental health of medical students during the COVID-19 Epidemic (Preprint)

2020 ◽  
Author(s):  
Fei Wang
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

scholarly journals Associative stigma among mental health professionals in Singapore: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028179 ◽  
Author(s):  
Louisa Picco ◽  
Sherilyn Chang ◽  
Edimansyah Abdin ◽  
Boon Yiang Chua ◽  
Qi Yuan ◽  
...  
Keyword(s):  
Mental Health ◽  
Job Satisfaction ◽  
Latent Class Analysis ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Online Survey ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Class Analysis ◽  
Associative Stigma

Objectives(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.DesignCross-sectional online survey.ParticipantsDoctors, nurses and allied health staff, working in Singapore.MethodsStaff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.ResultsThe latent class analysis revealed that items formed a three-class model where the classes were classified as ‘no/low associative stigma’, ‘moderate associative stigma’ and ‘high associative stigma’. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.ConclusionAssociative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.

The Patterns of Caregiving Activities for Family Caregivers of Older Adults in Hong Kong: An Exploratory Latent Class Analysis

2020 ◽  
Author(s):  
Jing Huang ◽  
Pui Hing Chau ◽  
Edmond Pui Hang Choi ◽  
Bei Wu ◽  
Vivian W Q Lou
Keyword(s):  
Older Adults ◽  
Hong Kong ◽  
Activities Of Daily Living ◽  
Latent Class Analysis ◽  
Family Caregivers ◽  
Caregiver Burden ◽  
Daily Living ◽  
Latent Class ◽  
Class Analysis ◽  
High Demand

Abstract Objectives This study identified the classes (i.e., patterns) of caregivers’ activities, based on their engagements in caregiving activities, and explored the characteristics and the caregiver burden of these classes. Methods This study was a secondary analysis of a cross-sectional survey on the profiles of family caregivers of older adults in Hong Kong. A latent class analysis approach was adopted to classify family caregivers (N = 932) according to their routine involvements in 17 daily caregiving activities: 6 activities of daily living (ADLs) and 8 instrumental activities of daily living activities (IADLs) in addition to emotional support, decision making, and financial support. Multinomial logistic regression and multiple linear regression illuminated the characteristics of the classes and compared their levels of caregiver burden. Results The family caregivers fell into 5 classes: All-Round Care (High Demand, 19.5%), All-Round Care (Moderate Demand, 8.2%), Predominant IADLs Care (High Demand, 23.8%), Predominant IADLs Care (Moderate Demand, 32.5%), and Minimal ADLs and IADLs Care (Low Demand, 16.0%). These classes exhibited different characteristics in terms of care recipients’ cognitive statuses and caregiver backgrounds. The levels of caregiver burden differed across classes; the All-Round Care (High Demand) class experienced the highest levels of caregiver burden. Discussion This study contributes to existing scholarship by turning away from a predefined category of care tasks to explore the patterns of caregiving activities. By identifying caregiving activity patterns and understanding their associated characteristics and caregiver burden, prioritizing and targeting caregiver support interventions better is possible.

scholarly journals Trajectories of Depressive Symptoms among Multicultural Adolescents in Korea: Longitudinal Analysis Using Latent Class Growth Model

2020 ◽  
Vol 17 (21) ◽  
pp. 8217
Author(s):  
Min Kyung Song ◽  
Ju Young Yoon ◽  
Eunjoo Kim
Keyword(s):  
Depressive Symptoms ◽  
Latent Class Analysis ◽  
Predictive Factors ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Panel Study ◽  
Class Analysis ◽  
Early Screening ◽  
Care Plans ◽  
Latent Class Growth

The purpose of this study was to investigate the trajectory of depressive symptoms in multicultural adolescents using longitudinal data, and to identify predictive factors related to depressive symptoms of multicultural adolescents using latent class analysis. We used six time-point data derived from the 2012 to 2017 Multicultural Adolescents Panel Study (MAPS). Latent growth curve modeling was used to assess the overall features of depressive symptom trajectories in multicultural adolescents, and latent class growth modeling was used to determine the number and shape of trajectories. We applied multinomial logistic regression analysis to each class to explore predictive factors. We found that the overall slope of depressive symptoms in multicultural adolescents increased. Latent class analysis demonstrated three classes: (1) high-increasing class (i.e., high intercept, significantly increasing slope), (2) moderate-increasing class (i.e., moderate intercept, significantly increasing slope), and (3) low-stable class (i.e., low intercept, no significant slope). In particular, we found that the difference in the initial intercept of depressive symptoms determined the subsequent trajectory. There is a need for early screening for depressive symptoms in multicultural adolescents and preparing individual mental health care plans.

scholarly journals Application of Latent Class Analysis in Assessing the Mental Health of Medical Students During the COVID-19 Epidemic

2021 ◽  
Author(s):  
Zhuang Liu ◽  
Yue Zhang ◽  
Ran Zhang ◽  
Rongxun Liu ◽  
Lijuan Liang ◽  
...  
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

Abstract Background: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.

Sign in / Sign up

Export Citation Format

Share Document