The effectiveness of Balint group work on the quality of work life, resilience, and nurse–patient communication skills among psychiatric nurses: a randomized controlled trial

BackgroundBalint group training has gained popularity in medical practices as an intervention designed to enhance the quality of life, well-being, and communication skills of healthcare practitioners. Psychiatric nurses, in particular, encounter distinct challenges and stressors inherent in their profession, necessitating the development and implementation of effective interventions to assist them in coping with the difficulties they experience. In this vein, the current study aimed to investigate the effectiveness of Balint group training on quality of work life, resilience, and nurse–patient communication skills among psychiatric nurses.MethodsThirty psychiatric nurses from Razi Hospital in Tehran were recruited via the purposeful sampling method in 2022 and were randomly assigned to either the Balint group, consisting of eight weekly one-hour training sessions, or a control group. Participants completed the Walton Quality of Work Life Questionnaire, Connor-Davidson Resilience Scale, and Communication Skills Scale before and after the intervention. The data were analyzed using the Analysis of Covariance (ANCOVA).ResultsThe study found no significant differences between the Balint group and the control group in terms of quality of work life, resilience, and nurse–patient communication skills.ConclusionFindings suggest that Balint group training was not an effective intervention for improving the well-being and communication skills of psychiatric nurses. However, the study highlights the need for further investigation into the potential factors that may explain the lack of significant gains and offers insights for future research in this area

The effect of escitalopram in treating mild to moderate depressive disorder and improving the quality of life in patients undergoing coronary artery bypass grafting – a double-blind randomized clinical trial

IntroductionChronic depression and anxiety can be a risk factor for coronary aArtery bypass grafting (CABG) and is an emerging factor after coronary artery disease when the patient is admitted to the hospital and after surgery. We aimed to assess the effect of Escitalopram in treating mild to moderate depressive disorder and improving the quality of life in patients undergoing CABG.MethodsIn this randomized clinical trial, 50 patients undergoing CABG referred to Tehran Heart Hospital from January 2021 to May 2021 and were suffering from mild to moderate depression were randomly assigned to one of the two groups of Escitalopram or placebo. The level of depression was assessed based on Beck’s depression inventory and the quality-of-life status and its domains were assessed based on the SF-36 questionnaire in 2 groups. Measurements were obtained at baseline and at four and eight weeks after treatment. Chi-square, Fisher’s exact, paired, and Wilcoxon tests or ANOVA were used as appropriate.ResultsThere was no significant difference between the level of depression between the two study groups at baseline (P=0.312). There was no significant difference between the quality of life and its domains in the two study groups at baseline (P=0.607). However, the most important effect of Escitalopram was reducing depression scores in the intervention group at weeks 4 and 8 after treatment compared to the placebo group (P<0.001). The quality of life and its domains were significantly higher in the Escitalopram group eight weeks after treatment (P=0.004). The amount of drug side effects at 2 and 4 weeks after treatment had no significant difference between the groups (P>0.05).ConclusionEscitalopram was effective in treating mild to moderate depressive disorder and improving quality of life in patients undergoing CABG.Clinical trial registrationhttps://irct.behdasht.gov.ir/, identifier IRCT20140126016374N2

Upper Endoscopic Findings in Children with Recurrent Abdominal Pain: High Prevalence of Hiatus Hernia

Objective: Recurrent abdominal pain (RAP) by itself is one of the common reasons in child-aged patients to refer to a clinician. Some of these patients are presented with more serious features, so-called the “red flag”. The most important issue in management of RAP is to distinguish the type of it, whether it is functional or organic. In this study we aimed to assess the redundancy of red-flagged RAP with findings of esophago-gastrodeudonoscopy. Methods: In a 2 year prospective study 150 consecutive children with RAP who showed red flags underwent esophago-gastro-deudonoscopy. The prevalence of each finding was recorded. Overall positive predictive value of predicting an endoscopic finding while having a red-flag was calculated. Findings: Among all the patients, 126 cases showed at least a positive finding in their endoscopy that corresponded to the positive predictive value of 84% for predicting the presence of an endoscopic finding according to red flags. Interestingly, 20% of patients showed hiatus hernia when surveyed. Conclusion: Comprehensive physical examination is needed to avoid performing esophago-gastrodeudonoscopy without indication in patients with recurrent abdominal pain

Spatial modeling of geogenic indoor radon distribution in Chungcheongnam-do, South Korea using enhanced machine learning algorithms

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent the construction of unsafe buildings. In this study, an indoor radon potential map of Chungcheongnam-do, South Korea, was generated using a group method of data handling (GMDH) algorithm based on local soil properties, geogenic, geochemical, as well as topographic factors. To optimally tune the hyper-parameters of GMDH and enhance the prediction accuracy of modelling radon distribution, the GMDH model was integrated with two metaheuristic optimization algorithms, namely the bat (BA) and cuckoo optimization (COA) algorithms. The goodness-of-fit and predictive performance of the models was quantified using the area under the receiver operating characteristic (ROC) curve (AUC), mean squared error (MSE), root mean square error (RMSE), and standard deviation (StD). The results indicated that the GMDH-COA model outperformed the other models in the training (AUC = 0.852, MSE = 0.058, RMSE = 0.242, StD = 0.242) and testing (AUC = 0.844, MSE = 0.060, RMSE = 0.246, StD = 0.0242) phases. Additionally, using metaheuristic optimization algorithms improved the predictive ability of the GMDH. The GMDH-COA model showed that approximately 7 % of the total area of Chungcheongnam-do consists of very high radon-prone areas. The information gain ratio method was used to assess the predictive ability of considered factors. As expected, soil properties and local geology significantly affected the spatial distribution of radon potential levels. The radon potential map produced in this study represents the first stage of identifying areas where large proportions of residential buildings are expected to experience significant radon levels due to high concentrations of natural radioisotopes in rocks and derived soils beneath building foundations. The generated map assists local authorities to develop urban plans more wisely towards region with less radon concentrations

A country-wide assessment of Iran's land subsidence susceptibility using satellite-based InSAR and machine learning

Land subsidence (LS), which mainly results from poor watershed management, is a complex and nonlinear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road or fault, rainfall, geology and land use) into a machine learning algorithm-based artificial neural network (ANN), and a powerful group method of data handling (GMDH). The total dataset includes historical LS and non-LS locations, identified by the interferometric synthetic aperture radar (InSAR). The whole dataset was divided into two subsets at a ratio of 70:30 for training and validating the model, respectively. ANN- and GMDH-based LS maps were evaluated using receiver-operator characteristic (ROC) curves. The information gain ratio (IGR) was calculated to determine the relative importance of the conditioning factors. The results showed that all of the considered factors contributed significantly to the LS mapping in Iran, with geology having the strongest impact. According to the ROC curve analysis, both ANN and GMDH-based LS maps were accurate, but the map obtained by the GMDH approach had a higher accuracy than that of ANN. Southwestern, northeastern and some parts of the central region of Iran were shown to be susceptible to LS in the future. According to the GMDH susceptibility map, 10% of Iran exhibits high or very high susceptibility to LS in the future. The provinces of Hamedan and Khouzestan had the highest percentage of areas at risk of LS. According to the InSAR deformation map, 39%, 20%, 25%, 13% and 3% of the investigated areas are subject to a yearly LS of −1 to −2.5, −2.5 to −5, −5 to −7.5, −7.5 to −10 and −10 to −20 cm, respectively. The province of Razavi Khorasan in the northeast of Iran had the largest area (about 3500 km2) vulnerable to LS occurrence. Based on the LS susceptibility map, the provinces of Ardebil, Kurdistan, West and East Azerbaijan, Sistan and Baluchistan and Kermanshah, although not currently undergoing a high rate of LS, will be at high risk of severe LS in the future