Physician burnout is a global concern that can lead to exhaustion, ineffectiveness, and poor health outcomes. Burnout has been linked to a variety of societal and professional variables worldwide. This cross-sectional, multi-centered study was conducted by face-to-face interviews between April 2019 and December 2021 at sixty-two (62) tertiary level hospitals to identify potential risk factors for burnout among Bangladeshi physicians, which is essential for preventing adverse impact on their well-being, improving overall quality of life, and facilitating measures to manage stress and maintain a healthy work-life balance. A simple random sampling technique in conjunction with a structured questionnaire was used to collect a total of 1434 responses, assuming 20 % of the sample as non-responsive. Univariate, bivariate, and multinomial logistic regression statistical analyses were performed to determine the risk factors and associate the level of severity. The distribution of burnout status differs significantly at distinct covariate levels, such as working place, working hour, prevalence of potential interpersonal conflicts (subsequent increase of adjusted odds ratios i.e.; 6.52, 8.82, 11.41, and 37.07 is observed for physicians having interpersonal conflicts with both co-workers & family members), job dissatisfaction, annoyed feeling while dealing with patients (adjusted odds ratios are 529.68, 518.26,983.87 and 849.57 respectively) and some other significant factors of the physicians. This study also reveals that the female physicians, physicians with age 40–49, physicians with additional liabilities, physicians with job dissatisfaction, less salary compared to the workload, less flexibility and security at the job sector as well as obese physicians are at high risk of burnout. These results are statistically significant with a p value ≤ 0.05. To reduce burnout of Bangladeshi physicians, it is necessary to address the risk factors, create supportive workplaces, maintain a healthy work-life balance, provide opportunities for self-care, and promote mental health.

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations.

Aim: The novel coronavirus disease 2019 (COVID-19) has already hit Bangladesh, and various control measures have been taken to flatten the epidemic curve. Due to the current demographic distribution in Bangladesh, young adults are vital to the demography of the country. Therefore, their precautionary behavior is very important to ensure the success of preventive policies. This exploratory study examined the differences in the adoption of precautionary behaviors among young adults, and estimated and compared the predictors of precautionary behavior adoption among young adults living in the capital city Dhaka and a nearby district, Tangail. Subject and Methods: A total of 350 respondents from each district participated in the study. ANOVA and two-sample t-tests were utilized to detect differences in precautionary behavior across demographic groups of young adults, and quantile regression modeling was used to find the predictors of adopting precautionary behaviors and to compare these predictors between the two districts. Results: Individuals who had a postgraduate education and had good mental health tended to show better precautionary behaviors in Dhaka. Female respondents from Tangail who had no psychological distress took precautionary behaviors more often than their counterparts. However, no significant differences in the adoption of precautionary behaviors to prevent COVID-19 among young adults were found between the two districts. Better self-control ability, higher education and good mental health emerged as factors that significantly shaped the precautionary behaviors of young adults in this study. Conclusion: Having better knowledge did not ensure better adoption of precautionary behaviors among the participants. In effect, the government’s strong intervention to keep people at home and distant from each other and continued lockdown for several more days are probable immediate solutions. At the same time, the economic burden on lower-income people should be addressed.

Objectives This study is aimed to identify the awareness and behavioral perspective on COVID-19 between urban and rural people of Bangladesh during the period of outbreak. Methods A community-based cross-sectional study was conducted in 30 districts of Bangladesh, where 322 participants from urban and 312 from rural area. Participants were selected through convenience sampling. Results Rural people are found to be much more inter connected to receive information from neighbor. Regarding the incubation periods and the general symptoms, knowledge differs significantly from urban to rural. Even their precautionary and transmission knowledge is found to associate in most of the cases. During this outbreak, urban people significantly increase their religious habits and also believe that there will some major change of life after outbreak. Conclusions The study reflected that health education program needed to aware about COVID-19 in both urban and rural in Bangladesh that helps in formulating and executing communication and outbreak management. Amaçlar Bu çalışma, salgın döneminde Bangladeş'in kentsel ve kırsal kesiminde yaşayan insanlar arasında COVID-19 hakkında farkındalık ve davranışsal bakış açısını belirlemeyi amaçlamaktadır. Yöntemler Bangladeş'in 30 ilçesinde, kentsel alanlardan 322 ve kırsal alandan 312 katılımcının bulunduğu topluluk temelli kesitsel bir çalışma yürütülmüştür. Katılımcılar uygun örnekleme yoluyla seçildi. Sonuçlar Kırsal kesimde yaşayan insanların komşularından bilgi almak için birbirleriyle çok daha bağlantılı oldukları görülmüştür. Kuluçka dönemleri ve genel semptomlarla ilgili olarak, bilgi kentselden kırsana önemli ölçüde farklılık göstermektedir. İhtiyati ve aktarım bilgilerinin bile çoğu durumda ilişkilendirildiği görülmektedir. Bu salgın sırasında, şehirli insanlar dini alışkanlıklarını önemli ölçüde artırıyor ve ayrıca salgının ardından bazı büyük yaşam değişiklikleri olacağına inanıyor. Sonuçlar Çalışma, sağlık eğitimi programının Bangladeş'te hem kentsel hem de kırsalda iletişim ve salgın yönetiminin formüle edilmesine ve yürütülmesine yardımcı olan COVID-19 hakkında bilgi sahibi olması gerektiğini gösterdi.

This chapter explores residual diagnostics and overdispersion in Generalized Linear Models (GLMs), with a focus on logistic and Poisson regressions. Beginning with the definition and visualization of residuals, including binned residual plots and DHARMa diagnostics, it underscores the importance of assessing model fit beyond classical deviance statistics. It then addresses overdispersion, a common violation in GLMs where observed variance exceeds theoretical expectations. Both frequentist (quasi-likelihood, negative binomial, and zero-inflated models) and Bayesian approaches (ZIBB and ZINB) are introduced to flexibly handle overdispersion and excess zeros. Using clinical data from the All of Us study, this chapter provides practical modeling guidance, comparison of model outputs, and insights into the implications of ignoring dispersion and heterogeneity in biomedical research.