Academic resilience, self-efficacy, and motivation: the role of parenting style.

Previous research has found that parenting style influences academic resilience. Nonetheless, few studies have focused on the mechanism underlying the relationship between parenting style and academic resilience. This study aims to examine the relationship between adolescents' parenting style and academic resilience, drawing upon the framework of Social Cognitive Theory. Specifically, it wants to explore the mediating roles of self-efficacy and academic motivation in this relationship. The participants were 518 students chosen at random from educational institutions in the Chinese provinces of Zhejiang, Shanghai, and Jiangsu. Social Cognitive Theory was the theoretical foundation for the study, and the Parental Authority Questionnaire was used to measure parenting style. Out of the respondents, 55.5% were male and 45.5% female. The student allocation in the study sample was as follows: 62.34% undergraduate, 28.22% master's, and 9.44% PhD. More than 60% of participants were over 25 years old. Moreover, the findings revealed that parenting style was directly and positively related to academic resilience. Parenting style was also found to be indirectly and positively related to academic resilience via self-efficacy and academic motivation, respectively, and sequentially. More crucially, it was discovered that the direct association was far lower than the indirect effects, with self-efficacy being the most effective. The study indicates a relationship between parenting style and academic resilience in adolescents, with self-efficacy and academic motivation acting as the main mediators. These findings emphasize the significance of these intermediary elements, implying that they play a larger role than the direct influence of parenting style alone.

Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior.

Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.

Spinopelvic imbalances are associated with worse postoperative functional outcomes in patients undergoing total hip arthroplasty.

PURPOSE: We aimed to investigate the relationship between spinopelvic imbalances and functional disabilities after total hip arthroplasty in an at least two years of follow-up. METHODS: Patients with normal sagittal alignment and normal motion (PI-LL < 10°, APP < 13°, ∆SS > 10°) were defined as control, and patients with any of sagittal alignment or motion abnormalities were defined as case groups. Visual Analog Scale, SF-36, Harris hip score, HOOS-JR, and complications were recorded. RESULTS: The differences of the means of Harris hip score, HOOS-JR, SF-36, and VAS score in the control and case groups were statistically significant. The mean of these parameters in patients with sagittal balanced (PI-LL < 10°) was much better than patients with sagittal unbalanced (PI-LL > 10°). Same results were noted in patients with decreased (∆SS < 10°) and normal spinopelvic motions (∆SS > 10°). CONCLUSION: Our observations indicate that spinopelvic imbalances are associated with worse postoperative functional outcomes in patients undergoing total hip arthroplasty.

Adolescent Obesity Modeling: A Framework of Socio-Economic Analysis on Public Health.

Bayesian Structural Equation Modeling (SEM-Bayesian) was applied across different research areas to model the correlation between manifest and latent variables. The primary purpose of this study is to introduce a new framework of complexity to adolescent obesity modeling based on adolescent lifestyle through the application of SEM-Bayesian. The introduced model was designed based on the relationships among several factors: household socioeconomic status, healthy food intake, unhealthy food intake, lifestyle, body mass index (BMI) and body fat. One of the main contributions of this study is from considering both BMI and body fat as dependent variables. To demonstrate the reliability of the model, especially in terms of its fitting and accuracy, real-time data were extracted and analyzed across 881 adolescents from secondary schools in Tehran, Iran. The output of this study may be helpful for researchers who are interested in adolescent obesity modeling based on the lifestyle and household socioeconomic status of adolescents.

Postpartum depression symptoms in survey-based research: a structural equation analysis.

BACKGROUND: Since the last decade, postpartum depression (PPD) has been recognized as a significant public health problem, and several factors have been linked to PPD. Mothers at risk are rarely undetected and underdiagnosed. Our study aims to determine the factors leading to symptoms of depression using Structural Equation Modeling (SEM) analysis. In this research, we introduced a new framework for postpartum depression modeling for women. METHODS: We structured the model of this research to take into consideration the Malaysian culture in particular. A total of 387 postpartum women have completed the questionnaire. The symptoms of postpartum depression were examined using the Edinburgh Postnatal Depression Scale (EPDS), and they act as a dependent variable in this research model. RESULTS: Four hundred fifty mothers were invited to participate in this research. 86% of the total distributed questionnaire received feedback. The majority of 79.6% of respondents were having depression symptoms. The highest coefficients of factor loading analysis obtained in every latent variable indicator were income (ß = 0.77), screen time (ß = 0.83), chips (ß = 0.85), and anxiety (ß = 0.88). Lifestyle, unhealthy food, and BMI variables were directly affected by the dependent variable. Based on the output, respondents with a high level of depression symptoms tended to consume more unhealthy food and had a high level of body mass indexes (BMI). The highest significant impact on depression level among postpartum women was unhealthy food consumption. Based on our model, the findings indicated that 76% of the variances stemmed from a variety of factors: socio-demographics, lifestyle, healthy food, unhealthy food, and BMI. The strength of the exogenous and endogenous variables in this research framework is strong. CONCLUSION: The prevalence of postpartum women with depression symptoms in this study is considerably high. It is, therefore, imperative that postpartum women seek medical help to prevent postpartum depressive symptoms from worsening.

Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach.

As postpartum obesity is becoming a global public health challenge, there is a need to apply postpartum obesity modeling to determine the indicators of postpartum obesity using an appropriate statistical technique. This research comprised two phases, namely: (i) development of a previously created postpartum obesity modeling; (ii) construction of a statistical comparison model and introduction of a better estimator for the research framework. The research model displayed the associations and interactions between the variables that were analyzed using the Structural Equation Modeling (SEM) method to determine the body mass index (BMI) levels related to postpartum obesity. The most significant correlations obtained were between BMI and other substantial variables in the SEM analysis. The research framework included two categories of data related to postpartum women: living in urban and rural areas in Iran. The SEM output with the Bayesian estimator was 81.1%, with variations in the postpartum women's BMI, which is related to their demographics, lifestyle, food intake, and mental health. Meanwhile, the variation based on SEM with partial least squares estimator was equal to 70.2%, and SEM with a maximum likelihood estimator was equal to 76.8%. On the other hand, the output of the root mean square error (RMSE), mean absolute error (MSE) and mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators. Thus, the predicted values of the SEM with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square. In conclusion, the higher values of R-square and lower values of MPE, RMSE, and MSE will produce better goodness of fit for SEM with Bayesian estimators.

mHealth Apps Assessment among Postpartum Women with Obesity and Depression.

BACKGROUND: Pregnancy has become the main constituent for women to become overweight or obese during the postpartum phase. This could lead women to suffer from postpartum depression as well. Information technology (IT) has become more prevalent in the healthcare industry. It offers patients the opportunity to manage their health conditions via the use of several applications, one being the mHealth applications. OBJECTIVE: The main purpose of this study is to experiment and understand the effects the mHealth applications (i.e., fitness and nutrition applications) have on the body mass index (BMI) and depression levels amongst postpartum women. METHODS: Online questionnaires were sent to postpartum women within one year after their pregnancy, of which 819 completed questionnaires were returned. The frequency of the mHealth applications usage was categorized into daily, weekly, rarely and never streams. Therefore, the frequency of use of the mHealth applications for BMI and depression levels was analyzed based on the available statistical data. Descriptive statistics, ANOVA, and Dunnet tests were applied to analyze the experimental data. RESULTS: Out of 819 respondents, 37.9% and 42.1% of them were overweight and obese, respectively. Almost 32.9% of the respondents were likely depressed, and 45.6% were at an increased risk. This study reports that only 23.4% and 28.6% of respondents never used the fitness and nutrition applications. The impact of the frequency of using the fitness applications on BMI and depression levels was obvious. This means that with the increased use of the fitness applications, there was also a significant effect in maintaining and decreasing the BMI and depression levels amongst Malaysians postpartum women. However, from the data of weekly and daily use of fitness applications, we found that the contribution toward the BMI and depression levels was high (p = 0.000). However, nutrition applications amongst the users were not significant within the main variables (p > 0.05). From the Dunnet test, the significance of using the fitness applications within the depression levels started from daily usage, whereas for BMI, it started from weekly usage. CONCLUSION: The efficiency of the fitness applications toward the BMI and depression levels has been proven in this research work. While nutrition applications did not affect the BMI and depression levels, some of the respondents were still categorized as weekly and daily users. Thus, the improvements in BMI and depression levels are associated with the types of mHealth app that had been used.

Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation.

In obesity modelling studies, researchers have been seeking to identify the effective indicators of obesity by using appropriate statistical or mathematical techniques. The main objective of the present study is addressed in three stages. First, a new framework for modelling obesity in university students is introduced. The second stage involves data analysis based on Bayesian Structural Equation Modelling (BSEM) for estimating the Body Mass Index (BMI) (representative of the obesity level) of students at three university levels: Bachelor, Master and PhD. In the third stage, the highest significant correlation is determined between the BMI and other variables in the research model that were found significant through the second phase. The data for this study were collected from students at selected Malaysian universities. The results indicate that unhealthy food intake (fast food and soft drinks), social media use and stress exhibit the highest weightage contributing to overweight and obesity issues for Malaysian university students.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling.

The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child's food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM) concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China) were considered. The framework includes two categories of data comprising the normal body mass index (BMI) range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment.