Chronic pain is a widespread condition that profoundly affects the daily functioning of many people worldwide, characterized by persistent sensory and emotional discomfort associated with real or perceived tissue injury. This study aims to predict chronic pain based on post-traumatic stress disorder (PTSD), alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables as correlates. The analysis included data from 234 males and 307 females experiencing chronic pain in Tehran province between 2022 and 2023. The classification results suggested that PTSD and alexithymia were the most significant predictors, followed by anxiety, depression, and pain catastrophizing. Six different machine learning (ML) classification techniques: Naive Bayes, Decision Tree, Gradient Boosting, Light Gradient Boosting Machine, Support Vector Machine, and Stochastic Gradient Boosting (SGB) were applied to examine a dataset detailing various dimensions associated with chronic pain. The findings in the study classified pain severity into three levels, low, medium, and high, based on quantiles and used six ML models to predict these classes. The SGB model outperformed the others, showing higher accuracy and F1 scores, particularly in predicting the medium pain class. SHAP analysis revealed that psychological factors such as alexithymia, anxiety, PTSD, depression, and stress were significant predictors of pain severity, while age and gender had less impact.

Background: During the COVID-2019 pandemic, the hospital staff was among the first to deal with this problem. Because of the high workload, the medical staff may experience psychological distress. In this regard, the present study was conducted to investigate the role of resilience and emotion regulation in managing the psychological distress of hospital staff during the COVID-2019 pandemic. Methods: In the present study, we searched English databases, such as Google Scholar, PubMed, Scopus, ScienceDirect, and Iranian databases, such as Iranian databases of Scientific Information Database (SID), Iranian Publications (Magiran), and Iranian Medical Sciences Articles from February 2020 to July 2021 with using the keywords of “coronavirus 2019”, “COVID-19”, “depression”, “anxiety”, “stress”, “resilience”, “emotion regulation”, “mental health”, “medical staff”, and “psychological distress”. We also used the PRISMA (The Preferred Reporting Items for Systematic Reviews and Meta-analysis) checklist to review and control the quality of the articles. Results: Studies have shown that resilience and emotion regulation strategies positively affect nurses, physicians, hospital staff, and medical students during the COVID-19 pandemic. Good resilience and using adaptive emotion regulation strategies have reduced their anxiety, depression, insomnia, and fear, which results in increased psychological well-being during the COVID-19 pandemic. Conclusion: Based on the reviewed articles, we found that COVID-19 can affect various aspects of the mental health of hospital staff. Given that COVID-19 is still mutating and it is impossible to predict a specific time for the end of this deadly virus, medical staff must receive the necessary training to improve resilience and emotion regulation strategies.

Background: Chronic pain’s influence on emotional well-being can be significant. It may evoke feelings of despair, frustration, nervousness, and melancholy in individuals, which often manifest as reactions to enduring pain and disruptions in their daily lives. In this study, we seek to perform Bootstrap Exploratory Graph Analysis (EGA) on the Persian Version of the Perth Alexithymia Questionnaire (PAQ) in a cohort of people with chronic pain.Methods: The research concentrated on the population of individuals encountering chronic pain within Tehran province from 2022 to 2023. Ultimately, the analysis comprised information from 234 male participants (with a mean age of 30.59, SD = 6.84) and 307 female participants (with a mean age of 30.16, SD = 6.65). After data collection, statistical analysis was conducted using the EGAnet2.0.4 package in R.4.3.2 software.Results: The outcome of bootstrapped EGA unveiled a two-dimensional configuration of the PAQ comprising Factor 1 denoted as negative difficulty in describing and identifying feelings (N-DDIF) and Factor 2 characterized as general-externally orientated thinking (GEOT), representing robust structural integrity and item consistency (all items have stabilities > 0.70).Conclusion: These findings endorse the validity of the PAQ, as evidenced by its confirmation in a broader sample using a novel methodology consistent with existing literature on two-factor decentering models.

Background and Purpose: Adolescents, because they are of sensitive ages, may undergo many psychological and physical changes that, if left unchecked, can lead to irreparable problems. In this regard, the present study was conducted to investigate the effectiveness of cognitive-behavioral group therapy on the psychological well-being and resilience of students ...

Background and Purpose: Irritable bowel syndrome (IBS) in children is characterized by chronic abdominal pain and alterations in bowel habits, such as frequent stools and constipation. This study aimed to evaluate the effectiveness of mindfulness-based cognitive therapy (MBCT) on alexithymia, pain intensity, and emotional regulation in children with IBS. Method: ...