Can Women Shelters Help Reduce Symptoms of PTSD and C-PTSD? Trajectories of PTSD Symptom Development Following Partner- and Family-Related Violence

Background: To protect women from Intimate partner violence (IPV), women’s shelters should not only provide emergency safety from IPV exposure, but also prolonged support that empowers women to build a life free from violence. The present study aims to investigate individual symptom development in association with residency at a women’s shelter. Method: Data were collected at four different timepoints, that is, enrolment (T1, N = 150), 3-months residency (T2, = 110), 6-months residency (T3, N = 68) and after relocation (T4, N = 63). Women were included from four Danish women’s shelters. The International Trauma Questionnaire (ITQ) was applied to test for post-traumatic stress disorder (PTSD) and Complex-PTSD (C-PTSD) at all timepoints. A paired sample t-test was used to test the mean symptom development, and a Latent Class Growth Analysis (LCGA) was applied to test for different classes of PTSD-trajectories. Logistic regression was applied to predict class membership from shelter-related variables and symptom severity, that is, length of residency, psychological counselling, revictimization and key symptoms of C-PTSD. Results: The prevalence of PTSD (31%) and C-PTSD (37.9%) was high at enrolment. Although t-tests suggested a significant decline in symptoms at follow-up, the LCGA revealed different classes of symptom development. The two-class model was found to be the best representation of data with low-symptom- and high-symptom profiles, respectively. Overall, the largest decline in symptoms occurred within the first 3 months of residency. Revictimization was high and was further found to predict class membership. However, when included in a multiple regression only symptom severity predicted the high-symptoms profile class. Discussion: Psychological treatment focussing on PTSD and C-PTSD is important for the women’s future well-being and safety. Reports on revictimization was alarmingly high, which emphasises a continuing need to protect women from psychological violence within the shelters. These findings should be replicated in larger samples before we can draw any conclusion.

Zero Initialized Active Learning with Spectral Clustering using Hungarian Method

Supervised machine learning tasks often require a large number of labeled training data to set up a model, and then prediction - for example the classification - is carried out based on this model. Nowadays tremendous amount of data is available on the web or in data warehouses, although only a portion of those data is annotated and the labeling process can be tedious, expensive and time consuming. Active learning tries to overcome this problem by reducing the labeling cost through allowing the learning system to iteratively select the data from which it learns. In special case of active learning, the process starts from zero initialized scenario, where the labeled training dataset is empty, and therefore only unsupervised methods can be performed. In this paper a novel query strategy framework is presented for this problem, called Clustering Based Balanced Sampling Framework (CBBSF), which is not only select the initial labeled training dataset, but uniformly selects the items among the categories to get a balanced labeled training dataset. The framework includes an assignment technique to implicitly determine the class membership probabilities. Assignment solution is updated during CBBSF iterations, hence it simulates supervised machine learning more accurately as the process progresses. The proposed Spectral Clustering Based Sampling (SCBS) query startegy realizes the CBBSF framework, and therefore it is applicable in the special zero initialized situation. This selection approach uses ClusterGAN (Clustering using Generative Adversarial Networks) integrated in the spectral clustering algorithm and then it selects an unlabeled instance depending on the class membership probabilities. Global and local versions of SCBS were developed, furthermore, most confident and minimal entropy measures were calculated, thus four different SCBS variants were examined in total. Experimental evaluation was conducted on the MNIST dataset, and the results showed that SCBS outperforms the state-of-the-art zero initialized active learning query strategies.

Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis

Disability is difficult to define succinctly. Current literature on disability has primarily focused on physical functional limitations. However, relying on a single dimension or index cannot accurately represent disability as the experience of disability is nuanced and complex. To address these gaps, this study aims to understand the multidimensional nature of disability among retired, community-dwelling older adults. Using a sample of 414 older adults between the ages of 72 and 106 years (M=84.84, SD=4.56), latent profile analysis was employed to identify classes based on five indicators of disability across three domains. The five indicators of disability included difficulties with activities of daily living (ADLs), cognitive impairment, physical impairment, sensory impairment, and participation restrictions. Three classes were found to represent the data best. The most favorable and highly functioning group comprised the highest number of participants (n=242, 59.5%). The next group, class 2 (n=157, 37.9%), was characterized by high physical impairment and ADL-difficulty. The smallest group, class 3 (n=15, 3.6%), had the highest ADL-difficulty and participation restrictions but drastically lower cognitive and sensory impairment. Multinomial logistic regression revealed that class membership was related to sociodemographic characteristics. Finally, class membership predicted several mental health outcomes such as depressive symptoms, positive affect, and life satisfaction in the expected direction. If supported by future work, these findings could inform practitioners in developing more specific interventions relevant to older adults based on their disability profiles. Understanding various combinations of disablement has potential implications for services and interventions to be tailored to individuals’ distinct disability-related needs.

Activity Engagement Patterns Among Older Chinese Immigrants

Activity engagement is a major component of well-being in later life. However, very few studies have focused on older immigrants who are often at risk for social isolation and psychological distress. We aim to map the pattern of activity engagement and examine its variations in relation to immigration-related factors and social aspects of neighborhoods in a representative sample of older Chinese immigrants. We used data from the Population Study of Chinese Elderly in Chicago (PINE), a population-based epidemiological study of US Chinese older adults that were conducted between 2011 and 2013 (N=3,157). Latent class analysis and multinominal regression analysis were conducted to identify activity engagement patterns and examine the associated factors. Four patterns of activity engagement were identified: restricted (15%), diverse (31%), informal social (32%), and community-based social (21%). Acculturation and family-oriented immigration differentiated the restricted from the diverse class membership. Positive attributes of social environment measured by social network size, positive social support, neighborhood cohesion, and sense of community were associated with the probabilities of class membership relative to the restricted class. Findings point to the importance of positive attributes of social environment in enhancing engagement with life among older Chinese immigrants. Efforts are needed to assist the vulnerable restricted group and recent older immigrants while meeting the demands of older immigrants who are less educated and less acculturated. Creating a supportive environment is important to provide information, access, and resources needed for activity engagement in the marginalized minority aging populations

A Dyadic Latent Profile Analysis of Older Couples’ Psychological, Relational, and Physical Health

The present study explored the heterogeneity of older couples’ psychological, relational, and physical health using a sample of 535 couples above the age of 62. A dyadic latent profile analysis was conducted to identify and predict unique clusters of couples’ relative psychological (depressive symptoms and daily hassles), relational (problematic affective communication and marital satisfaction), and physical health (number of health problems and self-reported health satisfaction). Predictors of class membership included relationship length, age, income, and hours worked outside the home. Results revealed 4 distinct classes: Happy & Healthy Together (63.5%), Individually & Relationally Strained (14.7%), Relationally Happy with Strained Wives (12.3%), and Relationally Happy with Strained Husbands (9.3%). Typology descriptions and predictors of class membership will be discussed. These findings highlight that health promotion efforts should be tailored to the specific psychological, relational, and physical health concerns of both partners rather than a one-size-fits-all approach.

Lifestyle differentiation among older adults: exploring the links between individuals’ behaviours, socio-demographic characteristics, health and wellbeing in later life

The association between everyday activities, health and subjective wellbeing in older adults has mostly been examined using different activities as separate variables. Which activities are likely to come together in individuals’ daily time-use patterns, or in what context, has not yet been analysed. This study looks at a broad range of spontaneously reported activities, their location and social context to identify latent behavioural classes. The data used in the study came from a sample of 200 non-institutionalised adults aged 65 and above. Activity data were collected using the Experience Sampling Method. Generalised structural equation modelling was used to identify the classes. Three distinctive behavioural classes, representing different lifestyles, emerged: passive domiciliary, active functional and social recreational. They constituted 30, 53 and 17 per cent of the sample, respectively. Class membership was related to individuals’ age, education and selected dimensions of health measured using the Nottingham Health Profile: energy levels and emotional response. There was consistency between the objectively measured class and an individual's subjective assessment of their physical and emotional health. While both class membership and subjective wellbeing were associated with health, the relationship between class and wellbeing was weak and fully explained by socio-demographic and health-related variables.

Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

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.

Self-Reported Mental Health During the COVID-19 Pandemic and its Association with Alcohol and Cannabis Use: A Latent Class Analysis

Background: Mental health problems and substance use co-morbidities during the COVID-19 pandemic are a public health priority. Identifying individuals at high-risk of developing these problems can directly inform mitigating strategies. We aimed to identify distinct groups of individuals (i.e., latent classes) based on patterns of self-reported mental health symptoms and investigate associations with alcohol and cannabis use.Methods: We used data from six successive waves of a web-based cross-sectional survey of adults aged 18 years and older living in Canada (6,021 participants). We applied latent class analysis to three domains of self-reported mental health: anxiety, depression, and loneliness. Logistic regression was used to characterize latent class membership, estimate the association of class membership with alcohol and cannabis use, and perform sex-based analyses.Results: We identified two distinct classes: 1) individuals with low scores on all three mental health indicators (no/low-symptoms) and 2) those reporting high scores (high-symptoms). Those at greater risk of being in the high-symptoms class were likely to be women (adjusted odds ratio (aOR) =1.34, 95%CI:1.18-1.52), people worried about getting COVID-19 (aOR=2.39, 95%CI:2.02-2.82), and those with post-secondary education (aOR=1.26, 95%CI:1.02-1.55). Asian ethnicity (aOR=0.78, 95%CI:0.62-0.97), married status (aOR=0.71, 95%CI:0.59-0.85), seniors (aOR=0.38, 95%CI:0.32-0.47), individuals in households with income higher than CAD$40,000: $40,000-$79,000 (aOR=0.73, 95%CI:0.60-0.90), $80,000-$119,000 (aOR=0.60, 95%CI:0.48-0.74) and $120,000+ (aOR=0.47, 95%CI:0.37-0.59) were at lower odds of being in the high-symptoms class. Individuals in the high-symptoms class were more likely to use cannabis at least once a week (aOR=2.25, 95%CI:1.90-2.67), drink alcohol heavily (aOR=1.69, 95%CI:1.47-1.95); and increase the use of cannabis (aOR=3.48, 95%CI:2.79-4.35) and alcohol (aOR=2.37, 95%CI:2.05-2.73) during the pandemic. Women in the high-symptoms class had higher odds of increasing alcohol use than men.Conclusions: We identified the determinants of experiencing high-symptoms of anxiety, depression, and loneliness, and found a significant association with alcohol and cannabis consumption. This suggests that initiatives and supports are needed to address mental health and substance use multi-morbidities, particularly regarding alcohol use in women.