Objective: Evaluative concerns perfectionism is related to both rumination and social anxiety. However, the mediating role of rumination between two types of perfectionism-namely, evaluative concerns perfectionism and personal standards perfectionism-and social anxiety has yet to be studied. Therefore, the objective of this study was to examine the mediating role of rumination on the association between perfectionism and social anxiety. Methods: A cross-sectional study was conducted among 450 Malaysian undergraduate students using self-report questionnaires. Results: Structural equation modeling (SEM) revealed that evaluative concerns perfectionism and rumination were significant positive predictors of social anxiety. Multimodel analysis revealed that rumination partially mediated the association between evaluative concerns perfectionism and social anxiety. Conclusions: The results suggested that evaluative concerns perfectionists were more likely to engage in rumination and were consequently more likely to experience social anxiety.
Suicide is an important public health problem for adolescents, and it is essential to increase our knowledge concerning the etiology of suicide among adolescent students. Therefore, this study was designed to examine the associations between hopelessness, depression, spirituality, and suicidal behavior, and to examine spirituality as a moderator between hopelessness, depression, and suicidal behavior among 1376 Malaysian adolescent students. The participants completed measures of depression, hopelessness, daily spiritual experience, and suicidal behavior. Structural equation modeling indicated that adolescent students high in hopelessness and depression, but also high in spirituality, had less suicidal behavior than others. These findings reinforce the importance of spirituality as a protective factor against hopelessness, depression, and suicidal behavior among Malaysian adolescent students.
Suicide is a substantial public health problem, and it remains a serious cause of death in the world. Therefore, this study was designed to examine the relationships between brooding, reflection, emotional intelligence (assessed by performance-based test), and suicidal ideation; the mediation role of emotional intelligence on the relationships between brooding and reflection with suicidal ideation; and the moderating role of suicidal history on the relationships between brooding, reflection, and emotional intelligence with suicidal ideation among Iranian depressed adolescents. The study consisted of a cross-sectional sample of 202 depressed adolescent inpatients from five public hospitals in Tehran, Iran completed measures of depression, rumination, emotional intelligence, and suicide attempt history as indices of suicidal ideation. Structural Equation Modelling estimated that depressed adolescent inpatients with high levels of brooding and reflective rumination, and low levels of emotional intelligence were more likely to report suicidal ideation. Moreover, emotional intelligence partially mediated the relationships between brooding and reflective rumination with suicidal ideation. Suicidal history moderated the relationships between brooding, reflection, and emotional intelligence with suicidal ideation. These findings reinforce the importance of emotional intelligence as an influencing factor against the deleterious effects of rumination styles and suicidal ideation. The results indicate that brooding and reflection have detrimental effects on suicidal ideation in depressed inpatients.
One of the worst environmental catastrophes that endanger the Australian community is wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and identify fire susceptibility in wildfire-prone regions. The use of machine learning (ML) algorithms is acknowledged as one of the most well-known methods for addressing non-linear issues like wildfire hazards. It has always been difficult to analyze these multivariate environmental disasters because modeling can be influenced by a variety of sources of uncertainty, including the quantity and quality of training procedures and input variables. Moreover, although ML techniques show promise in this field, they are unstable for a number of reasons, including the usage of irrelevant descriptor characteristics when developing the models. Explainable AI (XAI) can assist us in acquiring insights into these constraints and, consequently, modifying the modeling approach and training data necessary. In this research, we describe how a Shapley additive explanations (SHAP) model can be utilized to interpret the results of a deep learning (DL) model that is developed for wildfire susceptibility prediction. Different contributing factors such as topographical, landcover/vegetation, and meteorological factors are fed into the model and various SHAP plots are used to identify which parameters are impacting the prediction model, their relative importance, and the reasoning behind specific decisions. The findings drawn from SHAP plots show the significant contributions made by factors such as humidity, wind speed, rainfall, elevation, slope, and normalized difference moisture index (NDMI) to the suggested model's output for wildfire susceptibility mapping. We infer that developing an explainable model would aid in comprehending the model's decision to map wildfire susceptibility, pinpoint high-contributing components in the prediction model, and consequently control fire hazards effectively.
Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.
To examine the relationships between self-esteem, body-esteem, emotional intelligence, and social anxiety, as well as to examine the moderating role of weight between exogenous variables and social anxiety, 520 university students completed the self-report measures. Structural equation modeling revealed that individuals with low self-esteem, body-esteem, and emotional intelligence were more likely to report social anxiety. The findings indicated that obese and overweight individuals with low body-esteem, emotional intelligence, and self-esteem had higher social anxiety than others. Our results highlight the roles of body-esteem, self-esteem, and emotional intelligence as influencing factors for reducing social anxiety.
Given that the prevalence of social anxiety in obese individuals is high, it is necessary that we increase our knowledge about the related factors that cause social anxiety in obese individuals. The present study sought to examine the role of body esteem as a mediator between sedentary behaviour and social anxiety. The participants were 207 overweight and obese individuals who completed the self-report measures. The structural equation modelling displayed that obese individuals with sedentary behaviour and poor body esteem were more likely to show social anxiety. Body esteem partially mediated between sedentary behaviour and social anxiety. Our results highlight the role of sedentary behaviour and body esteem as promising avenues for reducing social anxiety in obese individuals.
To examine the moderating role of spirituality between hopelessness, spirituality, and suicidal ideation, 202 Iranian depressed adolescent inpatients completed measures of patient health, suicidal ideation, hopelessness, and core spiritual experience. Structural equation modelling indicated that depressed inpatients high in hopelessness, but also high in spirituality, had less suicidal ideation than others. These findings reinforce the importance of spirituality as a protective factor against hopelessness and suicidal ideation.
The relevance of the study of happiness and stress in nurses has been emphasized. In this sense, the intelligent use of hardiness is enable nurses to cope better with stress and contribute to being happier. This study aimed to examine the relationship among hardiness, perceived stress, and happiness in nurses. Moreover, we examined the mediator role of hardiness on the relationship between perceived stress and happiness in nurses. Our study revealed that hardi-attitude nurses evaluate situations as less stressful which results in a higher happiness. This study showed hardiness as being a protective factor against perceived stress and a facilitating factor for happiness in nurses. The findings could be important in training future nurses so that hardiness can be imparted, thereby giving them the ability to control their stress. Nursing is a stressful occupation with high levels of stress within the health professions. Given that hardiness is an important construct to enable nurses to cope better with stress and contribute to being happier; therefore, it is necessary we advance our knowledge about the aetiology of happiness, especially the role of hardiness in decreasing stress levels and increasing happiness. The present study sought to investigate the role of hardiness as a mediator between perceived stress and happiness. The participants, comprising 252 nurses from six private hospitals in Tehran, completed the Personal Views Survey, the Perceived Stress Scale, and the Oxford Happiness Inventory. Structural Equation Modelling (SEM) was used to analyse the data and answer the research hypotheses. As expected, hardiness partially mediated between perceived stress and happiness among nurses, and nurses with low levels of perceived stress were more likely to report greater hardiness and happiness. In addition, nurses with high levels of hardiness were more likely to report happiness. This study showed hardiness as being a protective factor against perceived stress and a facilitating factor for happiness in nurses. The findings could be important in training future nurses so that hardiness can be imparted, thereby giving them the ability to control their stress.
BACKGROUND: Given that happiness is an important construct to enable adolescents to cope better with difficulties and stress of life, it is necessary to advance our knowledge about the possible etiology of happiness in adolescents.
OBJECTIVES:The present study sought to investigate the relationships of emotional intelligence, depressive symptoms, and happiness in a sample of male students in Tehran, Iran.
MATERIALS AND METHODS: This cross-sectional study was conducted on a sample of high school students in Tehran in 2012. The participants comprised of 188 male students (aged 16 to 19 years old) selected by multi-stage cluster sampling method. For gathering the data, the students filled out assessing emotions scale, Beck depression inventory-II, and Oxford happiness inventory. Data analysis was carried out using descriptive and analytical statistics in statistical package for social sciences (SPSS) software.
RESULTS: The findings showed that a significant positive association existed between high ability of emotional intelligence and happiness (P < 0.01). Conversely, the low ability of emotional intelligence was associated with unhappiness (P < 0.01), there was a positive association between non-depression symptoms and happiness (P < 0.05), and severe depressive symptoms were positively associated with unhappiness (P < 0.01). High ability of emotional intelligence (P < 0.01) and non-depression symptoms (P < 0.05) were the strongest predictors of happiness.
CONCLUSIONS: These findings reinforced the importance of emotional intelligence as a facilitating factor for happiness in adolescences. In addition, the findings suggested that depression symptoms may be harmful for happiness in adolescents.
To better understand depression among adolescent university students, this study was designed to examine coping style as a potential mediator between perfectionism and depression. Participants comprised 510 undergraduate students from Malaysia. Structural Equation Modelling demonstrated that personal standards perfectionism and task-focused coping style were negatively associated with depression, while emotion-focused coping style, avoidant coping style, and evaluative concerns perfectionism were positively associated with depression. Multiple mediator modelling provided evidence that coping styles partially mediated the relationship between perfectionism and depression. These findings advance current knowledge by suggesting how perfectionism may contribute to depression and may inform the development of more effective prevention and intervention programs for depression.
Nursing is a stressful occupation, even when compared with other health professions; therefore, it is necessary to advance our knowledge about the protective factors that can help reduce stress among nurses. The present study sought to investigate the associations among problem-solving skills and hardiness with perceived stress in nurses. The participants, 252 nurses from six private hospitals in Tehran, completed the Personal Views Survey, the Perceived Stress Scale, and the Problem-Solving Inventory. Structural Equation Modeling (SEM) was used to analyse the data and answer the research hypotheses. As expected, greater hardiness was associated with low levels of perceived stress, and nurses low in perceived stress were more likely to be considered approachable, have a style that relied on their own sense of internal personal control, and demonstrate effective problem-solving confidence. These findings reinforce the importance of hardiness and problem-solving skills as protective factors against perceived stress among nurses, and could be important in training future nurses so that hardiness ability and problem-solving skills can be imparted, allowing nurses to have more ability to control their perceived stress.
Recent evidence suggests that suicidal ideation has increased among Malaysian college students over the past two decades; therefore, it is essential to increase our knowledge concerning the etiology of suicidal ideation among Malaysian college students. This study was conducted to examine the relationships between problem-solving skills, hopelessness, and suicidal ideation among Malaysian college students.
Recent evidence suggests that suicidal ideation is increased among university students, it is essential to increase our knowledge concerning the etiology of suicidal ideation among university students. This study was conducted to examine the relationships between problem-solving skills appraisal, hardiness, and suicidal ideation among university students. In addition, this study was conducted to examine problem-solving skills appraisal (including the three components of problem-solving confidence, approach-avoidance style, and personal control of emotion) as a potential mediator between hardiness and suicidal ideation.
One of the biggest barriers in treating adolescents with mental health problems is their refusing to seek psychological help. This study was designed to examine the relationships between two forms of perfectionism, self-concealment and attitudes toward seeking psychological help and to test the mediating role of self-concealment in the relationship between perfectionism and attitudes toward seeking psychological help among Malaysian high school students. The participants were 475 Malaysian high school students from four high schools in Kuala Lumpur, Malaysia. Structural equation modelling results indicated that high school students with high levels of socially prescribed perfectionism, high levels of self-concealment, and low levels of self-oriented perfectionism reported negative attitudes toward seeking psychological help. Bootstrapping analysis showed that self-concealment emerged as a significant, full mediator in the link between socially prescribed perfectionism and attitudes toward seeking psychological help. Moderated mediation analysis also examined whether the results generalized across men and women. The results revealed that male students with socially prescribed perfectionism are more likely to engage in self-concealment, which in turn, leads to negative attitudes toward seeking psychological help more than their female counterparts. The results suggested that students high in socially prescribed perfectionism were more likely to engage in self-concealment and be less inclined to seek psychological help.
This study was designed to examine the relationships between problem-solving skills, hardiness, and perceived stress and to test the moderating role of hardiness in the relationship between problem-solving skills and perceived stress among 500 undergraduates from Malaysian public universities. The analyses showed that undergraduates with poor problem-solving confidence, external personal control of emotion, and approach-avoidance style were more likely to report perceived stress. Hardiness moderated the relationships between problem-solving skills and perceived stress. These findings reinforce the importance of moderating role of hardiness as an influencing factor that explains how problem-solving skills affect perceived stress among undergraduates.
Anthropometry is a Greek word that consists of the two words "Anthropo" meaning human species and "metery" meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.