Five-factor model personality traits, exclusive breastfeeding, and self-efficacy: a mediational analysis.

BACKGROUND: Despite the World Health Organization's (WHO) emphasis on exclusive breastfeeding, the documents show a declining trend worldwide. Studies assert that the mother's personality traits appear to have an impact on this issue. This study aimed to investigate the potential influence of personality traits on exclusive breastfeeding, which might be channeled by self-efficacy as a mediator variable. METHODS: Data were analyzed from the cross-sectional study. The exclusive breastfeeding scale, the breastfeeding self-efficacy questionnaire, and the Five-Factor Model questionnaire (as follows: neuroticism, extraversion, openness experience, agreeableness, and conscientiousness) were completed by120 Iranian volunteer mothers with an infant aged 6-12 months referred to health centers in Shiraz (a major city in southern Iran) between May to December 2019. The structural equation modeling (SEM) approach was used to obtain the direct and indirect effects of personality traits and self-efficacy on exclusive breastfeeding. RESULTS: The study showed the significant direct effect of some personality traits (agreeableness, extraversion, and consciousness) and self-efficacy on exclusive breastfeeding. The indirect effect of extraversion on exclusive breastfeeding through self-efficacy was also obtained from the result of SEM analysis. The model fit the data satisfactorily, according to the fit indices criteria extracted from the mediational analysis. CONCLUSIONS: Self-efficacy appears to be a significant predictor of exclusive breastfeeding. Therefore, exclusive breastfeeding could be enhanced by safe education in pregnancy, reinforcing the self-efficacy of pregnant women and considering their personality traits.

Suicidal ideation amongst medical students in Shiraz, South of Iran: a cross-sectional study.

Suicide is a preventable act, but irreversible once committed; Hundreds of thousands of people commit suicide annually around the globe. One of the groups at high risk of suicide is medical students. Suicidal ideation (SI) is a forerunner to suicidal attempts which could be assessed. Since there are not enough data about this issue among Iranian medical students, we aimed to estimate the prevalence of SI and risk factors among Shiraz University of Medical Sciences (SUMS) undergraduate medical students. An institutional-based cross-sectional study was conducted from December 2022 to February 2023 in the medical school of SUMS. Students willing to participate were recruited by stratified random sampling technique. Data were collected using a self-administered questionnaire along with Beck Scale for Suicidal Ideation (BSSI) and Depression, Anxiety, and Stress Scale (DASS-21). Bivariate analysis and multivariate logistic regression model were carried out using BlueSky software. The study identified that 76 out of 308 attendees (24.7%, 95% CI: 19.9%-29.5%) reported experiencing suicidal ideation. Very severe depression [AOR= 26.705, 95% CI: (8.825 - 91.046)], Severe depression [AOR= 17.142, 95% CI: (5.567 - 58.121)], positive family history of psychiatric disorders [AOR= 4.181, 95% CI: (1.773 - 10.014)], comorbid mental illness [AOR= 2.502, 95% CI: (1.123 - 5.553)], were found to be statistically related to having SI. This study showed that one out of every four undergraduate medical students at SUMS has SI, which warns everyone to plan and act to prevent the loss of more lives. Depression, family history of psychiatric disorders, and comorbid mental diseases were found to be strongly associated with SI.

Assessing the effect of child's gender on their father-mother perception of the PedsQL™ 4.0 questionnaire: an iterative hybrid ordinal logistic regression/item response theory approach with Monte Carlo simulation.

BACKGROUND: This study aimed at investigating the possible confounding effect of children's gender on the parents' dyads perception of their child HRQoL at both item and scale levels of PedsQLTM4.0 questionnaire. METHODS: The PedsQL™ 4.0 Generic Core Scales were completed by 573 children and their father-and-mother dyads. An iterative hybrid ordinal logistic regression/item response theory model with Monte Carlo simulation was used to detect differential item functioning (DIF) invariance across mothers/fathers and daughter/sons. RESULTS: Assessing DIF across mother-daughter, father-daughter, mother-son, and father-son dyads revealed that although parents and their children perceived the meaning of some items of PedsQLTM4.0 instrument differently, the pattern of fathers' and mothers' report does not vary much across daughters and sons. CONCLUSION: In the Persian version of PedsQLTM4.0, the child's gender is not a confounding factor in the mothers' and fathers' report with respect to their daughters' and sons' HRQoL. Hence, paternal proxy-reports can be included in studies, along with maternal proxy-reports, and the reports can be combined short of concerning children gender, when looking at parent-child agreement.

A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism.

This manuscript aims to present the first item response theory (IRT) model within a pharmacometric framework to characterize the longitudinal changes of Aberrant Behavior Checklist (ABC) data in children with autism. Data were obtained from 120 patients, which included 20,880 observations of the 58 items for up to three months. Observed scores for each ABC item were modeled as a function of the subject's disability. Longitudinal IRT models with five latent disability variables based on ABC subscales were used to describe the irritability, lethargy, stereotypic behavior, hyperactivity, and inappropriate speech over time. The IRT pharmacometric models could accurately describe the longitudinal changes of the patient's disability while estimating different time-course of disability for the subscales. For all subscales, model-estimated disability was reduced following initiation of therapy, most markedly for hyperactivity. The developed framework provides a description of ABC longitudinal data that can be a suitable alternative to traditional ABC data collected in autism clinical trials. IRT is a powerful tool with the ability to capture the heterogeneous nature of ABC, which results in more accurate analysis in comparison to traditional approaches.

Power and Sample Size Calculations in Clinical Trials with Patient-Reported Outcomes under Equal and Unequal Group Sizes Based on Graded Response Model: A Simulation Study.

OBJECTIVES: To provide a valid sample size strategy based on simulation and to evaluate the statistical power in clinical trials with patient-reported outcomes (PROs) based on a polytomous item response theory model-the graded response model (GRM)-and to compare this framework with the classical test theory (CTT) approach. METHODS: One thousand randomized clinical trials were simulated using PRO based on the GRM and under various combinations of the number of patients in each arm, the group allocation ratio, the number of items and categories, and group effects. The power and sample size estimated in the simulations were then compared with those computed using the CTT framework. RESULTS: The results indicated that the impact of the most influential factors, including the number of patients, group allocation ratio, group effects, and the number of categories, on the power and sample size of the GRM-based and CTT-based approaches was similar. Nevertheless, the strong impact of the number of items on these issues distinguished the two approaches. CONCLUSIONS: It is crucial to use an adapted sample size formula in a GRM-based analysis because the classical formula designed for the CTT-based approach does not consider the impact of the number of items, which could result in an inadequately sized study and a decrease in power. Thus, when clinicians design a randomized clinical trial with polytomous PRO endpoints using classical sample size formula as the base, they should be aware of the possibility of making an incorrect clinical decision.

Testing parent dyad interchangeability in the parent proxy-report of PedsQL™ 4.0: a differential item functioning analysis.

PURPOSE: In child-parent agreement studies in the field of paediatric health-related quality of life (HRQoL), little attention has been paid to the effect of gender in parental proxy rating of children's HRQoL. This study aims to test the potential interchangeability of parent dyads in reporting children's HRQoL on both item and scale levels of the PedsQL™ 4.0 instrument, using the approach of differential item functioning (DIF). METHODS: The PedsQL™ 4.0 Generic Core Scales were completed by 576 father-and-mother dyads. A polytomous item response theory model, graded response model, was used to detect DIF across fathers and mothers. RESULT: Assessment at item level showed that fathers and mothers perceived the meaning of items of the PedsQL™ 4.0 consistently. Regarding the scale level, a moderate to high level of agreement was observed between mothers' and fathers' reports on all similar subscales. Although the significant mean score differences in total, physical and emotional functioning indicated that fathers gave higher scores to their children, the small effect size implied that this difference may not be practically meaningful. CONCLUSION: Our findings revealed that discrepancy in parent dyads in rating children's HRQoL is a "real" difference and not an artefact due to measurement non-invariance. Fathers were seen to have slightly different insights into their children, especially for emotional functioning, but overall the results were not all that different. This suggests that paternal proxy-reports can be included in studies along with maternal proxy-reports, and the two may be combined when looking at parent-child agreement. Parent-child agreement studies in Iran are not affected by parents' gender, and therefore, researchers may rely on the assumption of the interchangeability of fathers and mothers in these studies.

Examining exclusive breastfeeding in Iranian mothers using the five-factor model of personality traits.

OBJECTIVES: The initiation of exclusive breastfeeding and its continuation plays a vital role in maternal and child health. This study investigates the prediction of exclusive breastfeeding in Iranian mothers using the five-factor model. METHODS: A descriptive correlational study was conducted using cluster random sampling. and 120 mothers with children aged 6 to 12 months, referred to health centres of Shiraz University of Medical Sciences in Valfagr and Enghelab in Iran, participated in this cross-sectional study. The participants were requested to fill 3 questionnaire containing demographic questionnaire, the exclusive breastfeeding scale, and the Big Five factors (BFF) questionnaire of personality traits. The data were collected between May and December 2019 and analysed using Pearson's correlation coefficient and multiple regression. RESULTS: The results showed that the agreeableness trait had the highest score (mean score = 16.13, SD = 2.10) and the neuroticism trait had the lowest score (mean score = 12.13, SD = 2.68). The main results indicated a significant relationship between the extraversion trait and exclusive breastfeeding (r = 0.36, p < 0.01). In the regression analysis, the results were indicative of the positive prediction of exclusive breastfeeding for the extraversion (p < 0.01, ß = 0.43) and the conscientiousness traits (p < 0.05, ß = 0.18). CONCLUSIONS: Personality traits may potentially affect exclusive breastfeeding and could be a useful tool in reducing impediments to exclusive breastfeeding and in identifying mothers who need more mental support.

Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study.

Random selection of initial centroids (centers) for clusters is a fundamental defect in K-means clustering algorithm as the algorithm's performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in K-means clustering algorithm. As regards, there are no comparative studies comparing these methods in various aspects, the present paper compared three hybrid methods with K-means clustering algorithm using concepts of genetic algorithm, minimum spanning tree, and hierarchical clustering method. Although these three hybrid methods have received more attention in previous researches, fewer studies have compared their results. Hence, seven quantitative datasets with different characteristics in terms of sample size, number of features, and number of different classes are utilized in present study. Eleven indices of external and internal evaluating index were also considered for comparing the methods. Data indicated that the hybrid methods resulted in higher convergence rate in obtaining the final solution than the ordinary K-means method. Furthermore, the hybrid method with hierarchical clustering algorithm converges to the optimal solution with less iteration than the other two hybrid methods. However, hybrid methods with minimal spanning trees and genetic algorithms may not always or often be more effective than the ordinary K-means method. Therefore, despite the computational complexity, these three hybrid methods have not led to much improvement in the K-means method. However, a simulation study is required to compare the methods and complete the conclusion.

An Efficient and Effective Model to Handle Missing Data in Classification.

Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the "Bayesian additive regression trees" (BART) model is improved with the "Missingness Incorporated in Attributes" approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named "BART.m". As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.