The “Crisis and Concerns: A cross-sectional study on Junior Doctors during COVID-19 pandemic”.

Introduction: Doctors committed to the care of patients are trying to fulfill their duty in a difficult situation, but the consequential impacts of COVID-19 outbreak on Junior Doctors mental health are far too complex. Objective: The objective of this study was to gain insight into Junior Doctors’levels of concerns during a global pandemic of COVID-19. Methods: A quantitative cross-sectional study, utilizing a 23-item validated questionnaire was conducted on two hundred and fifty doctors of various disciplines and nationalities working on training and non-training posts. Results: The response rate was (77 %) and the levels of concern score among Junior doctors during COVID-19 pandemic in the NHS was found to be (41.35±4.9). With regard to gender differences, 32.5% of males and 27.5% females had a moderate concern score of 40 to 47. In regard to safety at work (85%) agreed that they are not safe, (100%) agreed that they are at risk to contract a COVID-19 infection at work and (100%) felt that they will transmit COVID-19 to their families but (95%) agreed that were obliged to take care of patients. In addition, (97.5%) doctors were not confident with the current infection control measures and (77.5%) felt frustrated with poor infection control training offered to them. Conclusion: Therefore, measures to strengthen personal protection and adequate support to Junior Doctors should be addressed urgently by the Healthcare system. KEYWORDS: COVID-19, Doctors, PPE, Concerns, Psychological impact.  

Parameters Estimation of the 3-Component Mixture Models of Exponential Family under Bayesian Paradigm

This thesis is concerned with the problem of estimating the parameters of the 3-component mixture models of the members of one parameter Exponential family using type-I right censored data under Bayesian paradigm. The models include: (i) Exponential distribution (ii) Rayleigh distribution (iii) Pareto distribution and (iv) Burr Type-XII distribution. In the Bayesian perspective, these 3-component mixtures of distributions get either no or least consideration in literature so far. Also, the reliability analysis of the 3-component mixtures of distributions is presented in this study. The expressions for the Bayes estimators and their posterior risks using the non-informative and the informative priors under squared error loss function, precautionary loss function and DeGroot loss function are derived. The censored sampling environment is considered due to its popularity in reliability theory and survival analysis. When prior information is available, elicitation of hyperparameters through prior predictive method based on predictive probabilities is given. The posterior predictive distribution for a future observation and the Bayesian predictive interval are constructed. In addition, the limiting expressions for the Bayes estimators and posterior risks under different loss functions are derived. To examine, numerically, the performance of the Bayes estimators under different loss functions, we have simulated Bayes estimates and posterior risks for different sample sizes, test termination times and parametric values. Further, to highlight the practical significance of each 3- component mixture, the Bayesian analysis of the real life mixture data sets is conducted. At the end of this thesis, the conclusion is presented. Some recommendations for future research are also given.