Growth Mixture Modeling Approach Research Articles

Exploring variations in subjective well-being among Norwegian retirees: A growth mixture modelling approach.

This study aimed to identify subgroups of Norwegian retirees with similar trajectories of subjective well-being (SWB) across the retirement transition and to explore how various factors influence these SWB trajectories. The study employed growth mixture modelling to analyze longitudinal data, exploring the trajectories of life satisfaction, positive affect and negative affect in retirement transition. It also examined the influence of factors such as demographic, living arrangement, pre-retirement work conditions, individual characteristics and available resources on these SWB trajectories. Contrary to the hypothesised three distinct SWB trajectories in retirement, the results more strongly support a single group solution for life satisfaction and positive affect. For negative affect, a two-group solution, consisting of one group with stable low and another with stable high negative affect, was preferred. The study identified several predictors of SWB trajectories. The findings suggest that retirement is a neutral life event with stable SWB trajectories. However, several factors significantly influence whether retirees experience higher or lower SWB in retirement. The findings of this study provide updated insights into the heterogeneity of the retirement experience, as well as highlight important factors for retirement SWB.

Obsessive-compulsive symptom trajectories from pregnancy through the postpartum: examining longitudinal course and risk factors during the COVID-19 pandemic.

The peripartum is a time of increased risk for the development or worsening of obsessive-compulsive (OC) symptoms. Existing evidence suggests that OC symptoms commonly surge immediately postpartum followed by a gradual recovery. Yet how societal conditions, such as a widespread public health crisis, may affect this pattern remains unknown. Increased OC symptom prevalence in the general population during the COVID-19 pandemic coupled with the pre-existing vulnerability of peripartum women suggests they may have been at particularly high risk for sustained symptoms. Thus, the aim of the current study was to examine trajectories in OC symptoms, particularly contamination concerns, in women who gave birth during the COVID-19 pandemic, and to look at risk and resiliency factors that may influence symptom course METHODS: A sample of 164 US pregnant women were recruited during the COVID-19 pandemic. They reported OC symptoms at 3 timepoints: April/May, 2020, July, 2020, and February, 2022. We used a growth mixture modeling approach to examine OC symptom trajectories from pregnancy through the postpartum across these timepoints, comparing the goodness of fit of models with 1-5 trajectory classes for overall OC symptoms and for contamination-related OC symptoms. For total OC symptoms, two classes of trajectories were identified: recovering (29.27%) versus stable low (70.73%) symptoms; for contamination symptoms alone, a subset of total symptoms, three trajectories were identified: recovering (8.54%), stable low symptoms (68.29%), and stable high symptoms (23.17%). Peripartum stress, but not sociodemographic or obstetric factors, predicted class membership. These findings suggest that peripartum OC symptoms, particularly related to contamination, may persist beyond the postpartum period in some women, particularly for women exposed to elevated stress in pregnancy and the postpartum.

6602 Classification of TSH Trajectories During Levothyroxine Treatment in the ELSA-Brasil Cohort

Abstract Disclosure: M.D. Ettleson: None. G.C. Penna: Consulting Fee; Self; Ipsen, Bayer, Inc.. W. Wan: None. I.M. Bensenor: None. N. Laiteerapong: None. A.C. Bianco: Consulting Fee; Self; Abbvie. Classification of TSH Trajectories During Levothyroxine Treatment in the ELSA-Brasil Cohort Introduction: Hypothyroidism is a common endocrine disorder for which levothyroxine (LT4) serves as the primary treatment. Periods during which thyroid stimulating hormone (TSH) levels are outside the normal range have been associated with adverse cardiovascular outcomes, including mortality. In practice, serial TSH levels are measured over time to ensure therapeutic LT4 treatment is maintained. However, there is no measure of the quality of treatment maintenance over time. TSH trajectory classification represents a novel approach to define the adequacy of LT4 treatment for hypothyroidism over time utilizing multiple observation points. Methods: With longitudinal clinical data, including thyroid function from a large prospective study, we aimed to define classes of TSH trajectories and examine changes in cardiovascular (CV) health markers over a 9 year study period with three observation points. Growth mixture modeling (GMM), including latent class growth analysis (LCGA), was used to classify LT4-treated individuals participating in the Longitudinal Study of Adult Health in Brazil (ELSA-Brasil) based on serial TSH levels. Repeated measure analyses were then utilized to measure within-class changes in blood pressure, lipid levels, hemoglobin A1c, and CV-related medication utilization. Results: From the 621 LT4-treated study participants, the best-fit GMM approach identified four TSH trajectory classes, as defined by their relationship to the normal TSH range: 1) High-high normal TSH, 2) Normal TSH, 3) Normal-to-low TSH, and 4) Low-to-normal TSH. Notably, the average baseline LT4 dose was lowest in the high-high normal TSH group (77.7 mcg, p <0.001). There were no significant differences in CV health markers between the classes at baseline. At least one significant difference in CV markers occurred in all classes, highlighted by the low-to-normal class, in which total and HDL cholesterol, triglycerides, and A1c all increased significantly (p = 0.049, p <0.001, p <0.001, and p = 0.001, respectively). Medication utilization increased in all classes over the study period. Conclusion: GMM/LCGA represents a viable approach to define and examine LT4 treatment by TSH trajectory. In the future, more comprehensive datasets will allow for more complex trajectory modeling and analysis of clinical outcome differences between trajectory classes. Defining the adequacy of LT4 treatment using a longitudinal scope could allow clinicians to more accurately communicate treatment progress and identify patients at risk for adverse CV outcomes. Presentation: 6/3/2024

Person-group fit predicted the joint trajectories of group cohesion and interpersonal conflicts among personnel during a prolonged mission in isolated and confined environment under sea: a parallel-process growth mixture modeling approach

Person-group fit predicted the joint trajectories of group cohesion and interpersonal conflicts among personnel during a prolonged mission in isolated and confined environment under sea: a parallel-process growth mixture modeling approach

TSH Trajectories During Levothyroxine Treatment in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) Cohort.

Thyroid-stimulating hormone (TSH) trajectory classification represents a novel approach to defining the adequacy of levothyroxine (LT4) treatment for hypothyroidism over time. This is a proof of principle study that uses longitudinal clinical data, including thyroid hormone levels from a large prospective study to define classes of TSH trajectories and examine changes in cardiovascular (CV) health markers over the study period. Growth mixture modeling (GMM), including latent class growth analysis (LCGA), was used to classify LT4-treated individuals participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) based on serial TSH levels. Repeated measure analyses were then utilized to assess within-class changes in blood pressure, lipid levels, hemoglobin A1c, and CV-related medication utilization. From the 621 LT4-treated study participants, the best-fit GMM approach identified 4 TSH trajectory classes, as defined by their relationship to the normal TSH range: (1) high-high normal TSH, (2) normal TSH, (3) normal to low TSH, and (4) low to normal TSH. Notably, the average baseline LT4 dose was lowest in the high-high normal TSH group (77.7 µg, P < .001). There were no significant differences in CV health markers between the classes at baseline. At least 1 significant difference in CV markers occurred in all classes, highlighted by the low to normal class, in which total and high-density lipoprotein cholesterol, triglycerides, and A1c all increased significantly (P = .049, P < .001, P < .001, and P = .001, respectively). Utilization of antihypertensive, antihyperlipidemic, and antidiabetes medications increased in all classes. GMM/LCGA represents a viable approach to define and examine LT4 treatment by TSH trajectory. More comprehensive datasets should allow for more complex trajectory modeling and analysis of clinical outcome differences between trajectory classes.

Multiple Trajectories and Predictors of Self-Esteem Change in Later Life: A Latent Growth Mixture Modeling Approach.

Applying latent growth mixture modeling (GMM), this study delves into the examination of self-esteem trajectories in a sample of 5,597 older adults over a nine-year period. Four distinct patterns of self-esteem changes have emerged: low, decreasing, increasing, and high. Additionally, the study explores the relationships between each trajectory and various predictors encompassing demographic factors, socioeconomic status, health, and interpersonal relationships. The findings highlight the significance of these factors in predicting the likelihood of an individual following a specific self-esteem trajectory. Notably, maintaining employment, fostering satisfactory social relationships, and being free of frequent depressive feelings emerged as strong predictors for the stability and increase of high self-esteem. Intriguingly, an average or above-average income was unexpectedly associated with lower levels of self-esteem. The study emphasizes the contribution of GMM to advancing aging research.

Physical activity trajectory during pregnancy and associations with maternal fatigue using a growth mixture modeling approach

The purpose of this study was to investigate the associations of physical activity trajectories with maternal fatigue. Pregnant women provided objectively assessed physical activity data by Pregnancy Physical Activity Questionnaire four times. Fatigue scale-14 was used to assess fatigue during pregnancy. Growth mixture modelling characterized physical activity trajectories across pregnancy. The generalized estimating equations was used to analyze the relationship between different physical activity profiles and fatigue in pregnant women. A total of 626 pregnant women were included in analysis in a teaching hospital in Nantong city. Fatigue (total, mental and physical) was not different between two groups based on total energy expenditure of PA (constantly high vs. constantly low). The pregnant women in “constantly high household PA” group had the higher fatigue compared to “constantly low household PA” (P < 0.05) and “constantly medium household PA” (P < 0.05). The pregnant women in “constantly high sport PA” group had lower fatigue compared to “constantly low sport PA” (P < 0.05). Household PA and sport PA were still an independent influencing factor for fatigue after controlling for confounding variables. Specifically, we observed that higher household PA and lower sport PA were associated with higher fatigue during pregnancy.

Trajectories of intrusive and deliberate rumination in young and middle-aged stroke survivors: A growth mixture modeling approach.

This study aimed to explore the relationship between intrusive and deliberate rumination, to identify distinct trajectories of intrusive and deliberate rumination, and to examine their predictors in young and middle-aged stroke survivors. This study employed a longitudinal design in which 200 young and middle-aged stroke survivors were investigated at 1-week pre-discharge (T0), 1 month (T1), 3 months (T2), and 6 months (T3) post-discharge. The Event-Related Rumination Inventory, Simplified Coping Style Questionnaire, and Perceived Social Support Scale were used for data collection. The results showed that intrusive rumination was positively correlated with deliberate rumination at T0 and T1, and negatively correlated with deliberate rumination at T3. Growth mixture modeling identified three classes of intrusive rumination: Stable-low, Declined, and Elevated group, and two classes of deliberate rumination: High-level and Low-level group. Furthermore, number of children or dysfunctions, type of stroke, family history of stroke, negative coping, and social support were found to predict intrusive rumination. These findings can help healthcare providers timely intervene on survivors in the Elevated and Stable-low intrusive rumination groups, and the Low-level deliberate rumination group.

Independent and joint trajectories of depression and anxiety symptoms among Chinese male sailors throughout a prolonged non-24-h rotating shift schedule at sea: a parallel-process growth mixture modeling approach

BackgroundThe predictive and protective effect of hardiness on mental health remains unclear among shift workers on non-24-h working schedules. The present study aimed to investigate the independent and joint trajectories of depression and anxiety symptoms and the role of hardiness during a prolonged period of non-24-h shift working schedule.MethodsFour hundred nine Chinese male sailors (working on 18-h watchstanding schedule) were recruited and completed all 5-wave tests through online questionnaires (at Day 1, 14, 28, 42, 55, respectively) during a 55-day sailing. The questionnaires included sociodemographic variables, hardiness, depression and anxiety symptoms. Independent and joint trajectories of depression and anxiety symptoms were estimated by latent growth mixture models. The effect of hardiness on trajectories was examined by logistic regression models.Results2 and 3 latent trajectories were identified for depression and anxiety symptoms, respectively. Based on initial levels and development trends, 3 distinct joint trajectories of depression and anxiety were identifed and named as: “Low-Inverted U” group (73.6%), “Moderate-Deterioration” group (6.9%), and “High-Stable” group (9.5%). Sailors with higher levels of hardiness were more likely to follow the “Low-Inverted U” trajectory of depression and anxiety symptoms (all p < 0.001).ConclusionsThere existed individual differences in the trajectories of depression and anxiety. Hardiness may have a protective effect that can prevent and alleviate depression and anxiety symptoms. Therefore, hardiness-based intervention programs are encouraged among the shift workers on non-24-h working and rest schedules.

Predictors of Variation in the Cognitive Function Trajectories among Older Adults Living Alone: A Growth Mixture Modeling Approach.

Considering the global aging population, this study investigates changes in cognitive function and predictive factors among older adults living alone. Using data collected from the Korean Longitudinal Study of Aging (KLoSA), the study examines 1217 participants to identify distinct cognitive change patterns and the variables affecting them. Two primary cognitive function change types emerged: "High-Level Declining Type" and "Low-Level Stable Type." Although the former initially displayed normal cognitive function, it gradually declined over a period of 14 years until it reached mild cognitive impairment (MCI) levels by the year 2020. While the latter group had lower cognitive function from the beginning and remained stable throughout the study. Older age, female gender, rural residence, lower education, lower income, unemployment, and higher levels of depression were linked to a higher likelihood of belonging to the "Low-Level Stable Type". The findings of these studies emphasize the need for proactive interventions and regular cognitive assessments for older individuals living alone, as cognitive impairment can develop even in individuals whose cognitive abilities are initially good. Also, tailored interventions should target specific demographic and socioeconomic groups to mitigate cognitive decline effectively.

Trajectories of cognitive flexibility through kindergarten and first grade: Implications for externalizing and internalizing behavior problems in the second grade.

Developmental delays in cognitive flexibility early in elementary school can potentially increase vulnerability for subsequent externalizing and internalizing psychopathology. The first goal of the current study was to identify latent subgroups of children characterized by different developmental trajectories of cognitive flexibility throughout kindergarten and first grade using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011 dataset. The second goal was to examine whether identified longitudinal developmental trajectories of cognitive flexibility could be associated with internalizing and externalizing behaviors in the second grade, while accounting for background child (age, gender, and Spanish-speaking) and family (family income and mother's education) covariates. The analytic sample consisted of 15,827 kindergarteners (51.20% male; 48.50% White, 13.5% Black/African American, 24.3% Hispanic/Latino, 7.60% Asian, and 6.1% other), who were approximately 5.62 years old (SD = 4.48 months) at the study's outset. Most children lived in households with medium family income of approximately $50,000-$55,000. Using a growth mixture modeling approach, our analyses identified normative (91.05%; 50.4% male) and delayed (8.95%; 59.4% male) cognitive flexibility groups and demonstrated that delayed developers have higher levels of externalizing and internalizing behaviors in the second grade, even after adjusting for background covariates. Our findings, in conjunction with research on cognitive flexibility training, suggest that caregivers may lower the risk for externalizing and internalizing behaviors in delayed developers by correcting inflexible thinking, encouraging alternative solutions, and providing emotional support when children face challenging problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Differences in the longitudinal change of behaviours related to dementia in long-term care: a growth mixture modelling approach

BackgroundThere is still a need for more information about the different trajectories of responsive behaviours that people living with dementia present in long-term care homes (LTC). Objective. This study identified subgroups of individuals with similar trajectories of responsive behaviours related to dementia in LTC and evaluated the role of demographic variables, depressive symptomatology, social engagement, cognitive functioning, and activities of daily living (ADL) on class membership.MethodsGrowth mixture models were run using data from the Continuing Care Reporting System.ResultsResults suggest that change in responsive behaviours is best represented by seven classes of trajectories. The largest class was composed of individuals who presented the lowest frequency of behaviours upon entry in LTC that increased at a slow linear rate. The other classes were composed of individuals who presented different frequencies of behaviours upon entry in LTC and varying rates of change (e.g., individuals who presented a low frequency of behaviours upon entry in LTC that increased at a linear rate followed by a decrease in the later months, individuals who presented a high frequency of responsive behaviours upon entry in LTC and that remained stable). Cognitive functioning, social engagement, depressive symptomatology, and ADL were markers of class membership.ConclusionsThese findings can help identify individuals at increased risk of presenting a high frequency of responsive behaviours and highlight interventions that could decrease behaviours in LTC.

Trajectories of college students' general self-efficacy, the related predictors, and depression: A piecewise growth mixture modeling approach

General self-efficacy plays a critical role in the development of college students, and mastering the development of students' general self-efficacy is helpful to explain students' behavior and psychological performance. Based on the data from the same cohort of college students for four consecutive years, this study used the piecewise growth mixture model to identify the developmental trajectories of general self-efficacy, built a multinomial logistic regression model to analyze the related predictors on different trajectories, and further compared the differences in depression symptoms in general self-efficacy trajectories. Three trajectories of college students' general self-efficacy were identified: stable-rising (8.7%), stable-decreasing (2.4%), and moderate and stable (88.9%). With the moderate and stable class as the reference, gender and extraversion are the predictors of students in the stable-increasing class; gender, extraversion, mother’s education level, and university tier significantly predict students who fall into the stable-decreasing class. With the stable-increasing class as the reference, gender also has a significant predictive effect on students who belong to the stable-decreasing class. However, age, ethnicity, siblings, hometown location, father’s education level, BMI, sleep, and major were not related predictors. Furthermore, mean differences in depression between latent classes of general self-efficacy trajectories were significant, and the depression scores of the stable-decreasing class were beyond the normal range in the third and fourth years. To promote college students' mental health, we suggest that colleges provide more specific psychological interventions to students based on the classification.

Developmental Trajectories and Predictors of Self-Esteem in Childhood: Growth Mixture Modeling Approach

Objectives: The four-wave longitudinal study explored developmental trajectories of self-esteem of elementary school children and examined the predictive factors such as the mother’s parenting behavior, children’s happiness and school adjustment associated with these trajectories.Methods: Trajectories of self-esteem from grades 1 to 4 were modeled using 1,659 children drawn from the Panel Study on Korean Children (PSKC) 2015~2018. Growth Mixture Modeling (GMM) analysis was used to estimate trajectories of self-esteem in grades 1~4. Using multiple logistic regression, the identified trajectories were then used to analyze whether the mother’s parenting, children’s happiness, and school adjustment affected the latent class classification.Results: First, the self-esteem of grades 1 to 4 revealed a linear growth pattern over time. Second, GMM identified four developmental trajectories in childhood self-esteem, of which the low-increasing, low-decreasing, high-increasing, or moderate-decreasing group reflected the normative trajectories. Third, the mother’s warm parenting, the academic performance of school adjustment, and the happiness of the children were significant in predicting the classification of latent class in the change trajectories of self-esteem.Conclusion: The results showed individual differences and heterogeneity in the development of selfesteem. Groups’ initial different developmental levels at first grade continue until grade 4. Developmental trajectories provide the direction for interventions by the level of self-esteem for elementary children. Especially, educational programs and interventions in mothers’ warm parenting, children’s happiness, and academic performance, which affect self-esteem trajectories, would be needed. These findings provide a meaningful foundation for designing programs and practical suggestions to encourage the self-esteem of elementary children.

Trajectories of Learned Helplessness in Maintenance Haemodialysis Patients and Their Predictive Effects on Self-Management: A Latent Growth Mixture Modeling Approach.

Learned helplessness (LH) is an essential psychological factor influencing maintenance haemodialysis (MHD) patients' health behaviour and is closely related to prognosis of the disease. This study aimed to identify potential trajectories of LH in MHD patients and assess their predictive role in self-management. This study was conducted in strict compliance with national laws, the Declaration of Istanbul, and the Declaration of Helsinki. A total of 347 MHD patients at a blood purification centre in Hunan Province, China, were selected as the study population. Four longitudinal surveys (baseline and second/fourth/sixth month after baseline) were conducted using the General Information Questionnaire for MHD patients, the Chinese version of the Learned Helplessness Scale for MHD patients, and the Self-Management Scale for Haemodialysis. Latent growth mixture model (LGMM) analysis was used to identify LH trajectories, and their predictors were analysed using multinomial logistic regression. The predictive role of LH trajectory on self-management was analysed using linear regression. This study identified three LH trajectories in MHD patients, named the "high-decreasing group" (57.9%), "low-increasing group" (21.3%), and "low-stability group" (20.7%). The results of the univariate analysis showed that sex (χ2=33.777, P < 0.001), age (χ2=10.605, P<0.05), and subjective social status (SSS) (χ2=12.43, P<0.01) were associated with LH trajectory classes. Multinomial logistic regression further demonstrated that gender, age, and SSS were predictors of different LH trajectories. The intercept and slope of the overall LH trajectory were negatively correlated with self-management (β=-0.273, P<0.001; β=-0.234, P<0.01). MHD patients show three different LH trajectories. The initial level and developmental rate of LH can negatively predict future self-management. It is necessary to screen MHD patients' LH and develop targeted interventions for them with different LH trajectories at specific stages.

Methods for analysing the relationship between poverty, parental work intensity, child emotional symptoms and conduct problems over time

This article exposes the methods employed to analyse the complex associations between poverty and work intensity over time on the longitudinal trajectories of mental health wellbeing in a cohort of children. This study used data from nine waves of birth cohort 1 of the Growing Up in Scotland (GUS) study (2005/06–2017/18) to fit a bivariate multilevel non-linear growth curve model for the change in conduct problems and emotional symptoms of children over time with the trajectories of poverty and parental work intensity over time as the main covariates of interest. We explain in detail: (a) how we arrive at valid measures for our outcome of interest by testing for longitudinal measurement invariance and (b) the principled approach of growth mixture modelling undertaken to derive our main covariates of interest. Both procedures are the preamble for the main model of interest that addresses the substantive research question of how changes over time in poverty and parental employment are associated with changes over time in children's wellbeing.•We expose the rationale behind and the procedures for implementing Longitudinal Measurement Invariance testing for the repeated measures of emotional and conduct problems.•We expose the rationale behind and the procedures for implementing a growth mixture modelling approach to derive longitudinal measures of poverty and work intensity.•We provide details of the bivariate growth curve model fitted to analyse the effect of the derived longitudinal measures of poverty and work intensity on the valid longitudinal measures of emotional and conduct problems.

Dynamic Trajectory of a Patient-Reported Outcome and Its Associated Factors for Patients with Chronic Heart Failure: A Growth Mixture Model Approach.

This study aimed to identify subgroups of chronic heart failure (CHF) patients with distinct trajectories of quality of life (QOL) and to identify baseline characteristics associated with the trajectories. Two-year, prospective, cohort study including 315 patients with CHF was conducted from July 2017. Information on QOL assessed by CHF-patient-reported outcomes measure (CHF-PROM) was collected at baseline, 6, 12, 18, and 24 months. Demographic and clinical variables were recorded at baseline. Growth mixture model was used to identify distinct trajectories of CHF-PROM and its physical, psychological, social, and therapeutic domains. Single factor analysis was employed to assess the factors associated with development of CHF-PROM over time. Two classes of overall score of CHF-PROM were identified: poorer (14.0%) and better (86.0%). Poorer class tended to be aged, have low diastolic blood pressure, have concomitant atrial fibrillation, diabetes, chronic obstructive pulmonary disease, cancers, and central nervous system diseases, and used nitrates. Three classes of physical scores were identified: unstable-poorer (5.2%), stable-poorer (29.4%) and better (65.4%). Age, NYHA grade, chronic obstructive pulmonary disease, combined with cancers and central nervous system diseases were related to the grouping. Poorer (8.6%) and better (91.4%) classes of psychological scores were identified. Poorer class tended to be female and had concomitant atrial fibrillation. Degenerate class (34.6%) and meliorate class (65.4%) of therapeutic scores were identified. Degenerate class tended to have concomitant chronic obstructive pulmonary disease and use less angiotensin converting enzyme inhibitors. We identified different classes with distinct trajectories of QOL that may help proper evaluate QOL and further improve its status for patients CHF.

Who Benefits from Universal SEL Programming?: Assessment of Second Step© Using a Growth Mixture Modeling Approach

Who Benefits from Universal SEL Programming?: Assessment of Second Step© Using a Growth Mixture Modeling Approach

Predicting children's differential trajectories of emotion dysregulation: A study on the intergenerational transmission of child and caregiver maltreatment

BackgroundChild maltreatment and caregiver history of abuse is negatively associated with the development of emotion regulation, and maltreatment in early childhood may be particularly disruptive. ObjectiveWe examined patterns of emotion dysregulation and the contribution of caregiver victimization and early maltreatment history on the development of distinct emotion dysregulation trajectories. ParticipantsThe current study sample (n = 1354) came from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN), a longitudinal study of the antecedents and consequences of child maltreatment. Children had a varied risk of maltreatment from high risk but not referred to child protective services to children who were removed from parental care. MethodWe employed a growth mixture modeling approach to model differential trajectories of children's emotion dysregulation from age four to age ten and assessed whether children's experiences of maltreatment prior to age four and caregiver histories of abuse were associated with children's probable class membership in the identified trajectories. ResultsWe identified three classes of emotion dysregulation trajectories: Well-Regulated, Increasingly Dysregulated, and Highly Dysregulated. Early experiences of multiple maltreatment types and caregiver history of abuse were associated with higher odds that children would be in the Increasingly Dysregulated and Highly Dysregulated classes compared to the Well-Regulated class. ConclusionThe current study extends the literature on the negative associations of caregiver histories of abuse and child experiences of multiple maltreatment types to children's emotion dysregulation, which may be long-lasting. Furthermore, our findings highlight the need for intervening early as a crucial component of breaking the intergenerational impact of maltreatment.

Understanding salient trajectories and emerging profiles in the development of Chinese learners’ motivation: a growth mixture modeling approach

Abstract Based on the theoretical framework of the L2 Motivational Self System (L2MSS), the present study aims to make a methodological contribution to L2 motivation research. With the application of a novel growth mixture modeling (GMM) technique, the study depicted developmental trajectories of three motivational variables (ideal L2 self, ought-to L2 self, and L2 learning experience) of 176 Chinese tertiary-level students over a period of two semesters. Results showed two to three salient classes with typical developmental patterns for the three motivational variables respectively, with which the study gained fresh insights into the developmental processes of motivation beyond the individual level. Our study further established three main multivariate profiles of motivation characterized by a distinct combination of different motivational variables. The findings extend our understanding of motivational dynamics, providing a nuanced picture of emergent motivational trajectories systemically. Additionally, GMM has shown to be an effective and applicable method for the identification of salient patterns in motivation development, which leads to practical implications.