mohamadhossein kaveh

Aggressive behavior is a common emotional manifestation among students of all grades. The present study aimed to establish the correlation between resilience, self-control, self-regulation, and decision-making style with aggressive behavior in adolescents.

In this cross-sectional study conducted in 2022 in Marvdasht, Iran, 469 students with a mean age of 15.48±1.74 years were recruited. Standard questionnaires were used to collect data on aggression, resilience, self-control, self-regulation, and decision-making style. The data were analyzed using SPSS version 22 and Amos version 24, with a significance level of P<0.05. Statistical methods including Pearson’s correlation coefficient, linear regression, and structural equation modeling (SEM) were employed for analysis.

The findings revealed significant negative correlations between self-regulation (r=-0.577, P<0.001), self-control (r=- 0.962, P<0.001), resilience (r=-0.984, P<0.001), and deliberative decision-making style (r=-0.571, P<0.001) with aggressive behavior in adolescents. Additionally, all variables except intuitive decision-making were found to have a significant association with aggressive behavior. Notably, resilience emerged as the most related variable to aggressive behavior. SEM analysis showed that standardized total effects of self-regulation, self-control, resilience, and deliberative decision-making on aggressive behavior were -0.500, -0.912, -0.632, and -0.565, respectively. The model fit indicators showed that the final model fit was acceptable (ᵡ2 / df=2.431, P=0.063, GFI=0.994, AGFI=0.969, CFI=0.999, NFI=0.989, RMSEA=0.055).

The study suggested all these variables, particularly resilience and self-control should be considered when planning interventions to reduce and control aggressive behavior.

Patient health engagement (PHE) is a dynamic and evolutionary process that involves improving health; however, little information is available on the PHE model. This study aimed to investigate health engagement and its relationship with social support, self-efficacy, and the quality of life in women with breast cancer.

This cross-sectional study was conducted on 129 breast cancer patients who were referred to a specialized medical clinic and met the inclusion criteria. Data were collected using the EORTC QLQ-br23 questionnaire, Communication and Attitudinal Self-efficacy Scale for Cancer (19-item), Perceived Social Support Scale, NCCN Distress Thermometer, and PHE Scale. The statistical methods used in this study were linear regression, ANOVA, CATREG (Categorical regression with optimal scaling using alternating least squares), and descriptive statistics.

The most commonly reported problem was tingling in hands and/or feet (43.3%). The median PHE score was 2. Additionally, 29.45% and 16.27% of the patients reported moderate and severe distress, respectively. The results of regression analysis showed that the PHE scale could predict social support, self-efficacy, quality of life and all of its dimensions (P<0.001). However, it is the strongest predictor of understanding and participating in care (P<0.001, r=0.485).

Given that the model can predict factors effective in improving cancer and interventions based on this model have not been performed in Iran, it is recommended that health interventions based on this model should be designed and implemented.