Unveiling the Heartbeat of Healing: Exploring Organizational Culture in a Tertiary Hospital's Emergency Medicine Department and Its Influence on Employee Behavior and Well-Being.

This study examined the organizational culture of an emergency medicine department (EMD) in a tertiary hospital in Karnataka, India, using a prospective cross-sectional design from January to February 2024. It aimed to identify the predominant and supporting organizational cultures within the EMD and their influence on employee behavior and well-being, including job satisfaction, burnout, stress levels, and coping strategies. A total of 82 participants, including physicians, emergency medical technicians, and nurses, completed the Organizational Culture Assessment Instrument (OCAI) and a self-designed questionnaire. Ethical clearance was obtained (IEC2-656). Clan culture emerged as the dominant culture (73.17%), emphasizing collaboration and adaptability, correlated with lower stress levels and high job satisfaction (90.78%). Emotional exhaustion was the most common burnout symptom (53.66%). The coping strategies varied, with employees in Clan cultures seeking social support, while those in Hierarchy cultures sought guidance from superiors. This study highlighted the significant role of organization culture in employee well-being and EMD effectiveness, influenced by social values like respect for authority. The limitations included single-setting analysis, an uneven subgroup representation, and a lack of qualitative insights. Future research should involve multiple hospitals and qualitative methods for a comprehensive understanding.

Application of machine learning models for risk estimation and risk prediction of classical swine fever in Assam, India.

The present study is aimed to develop an early warning system of Classical swine fever (CSF) disease by applying machine learning models and to study the climate-disease relationship with respect to the spatial occurrence and outbreaks of the disease in the north-eastern state of Assam, India. The disease incidence data from the year 2005 to 2021 was used. The linear discriminant analysis (LDA) revealed that significant environmental and remote sensing risk factors like air temperature, enhanced vegetation index, land surface temperature, potential evaporation rate and wind speed were significantly contributing to CSF incidences in Assam. Furthermore, the climate-based disease modelling was applied to relevant ecological and environmental risk factors determined using LDA and risk maps were generated. The western and eastern regions of the state were predicted to be at high risk of CSF with presence of significant hotspots. For the districts that are significantly clustered, the Basic reproduction number (R0) was calculated after the predicted results were superimposed onto the risk maps. The R0 value ranged from 1.04 to 2.07, implying that the eastern and western regions of Assam are more susceptible to CSF. Machine learning models were implemented using R statistical software version 3.1.3. The random forest, classification tree analysis and gradient boosting machine were found to be the best-fitted models for the study group. The models' performance was measured using the Receiving Operating Characteristic (ROC) curve, Cohen's Kappa, True Skill Statistics, Area Under ROC Curve, ACCURACY, ERROR RATE, F1 SCORE, and Logistic Loss. As a part of the suggested study, these models will help us to understand the disease transmission dynamics, risk factors and spatio-temporal pattern of spread and evaluate the efficacy of control measures to battle the economic losses caused by CSF outbreaks. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-023-00847-6.