Background Burnout, defined as mental and physical exhaustion, has been an issue for many medical students. Medical student burnout is associated with many factors such as academic pressure, sleep deprivation, exposure to patient suffering, and high academic demand. In this study, we assessed the prevalence of burnout symptoms among preclinical and clinical medical students studying at Qassim University in Qassim, Saudi Arabia. Results Three hundred thirty-six subjects entered the final data analysis with a majority between 18 and 24 years of age, of whom 56.5% was females and 43.5% was males. The overall burnout prevalence was 8%. The female gender was a significant predictor of emotional exhaustion and personal efficacy, (OR = 2.510; 95% Cl [1.845–3.415]; p value 0.000) and (OR = 1.434; 95% Cl [1.086–1.866]; p value 0.010), respectively. Conclusion Among medical students, burnout is common. The impact of gender on burnout was noticed; female gender was a significant predictor of emotional exhaustion and personal efficacy. Medical education style had no impact on burnout levels among medical students.

This paper considers robustness of Nonparametric Predictive Inference (NPI), in particular considering inference involving future order statistics. The concept of robust inference is usually aimed at development of inference methods which are not too sensitive to data contamination or to deviations from model assumptions. In this paper we use it in a slightly narrower sense. For our aims, robustness indicates insensitivity to small change in the data, as our predictive probabilities for order statistics and statistical inferences involving future observations depend upon the given observations. We introduce some concepts for assessing the robustness of statistical procedures to the NPI framework, namely sensitivity curve and breakdown point; these classical concepts require some adoption for application in NPI. Most of our nonparametric inferences have a reasonably good robustness with regard to small changes in the data.

BackgroundIsotretinoin has been used to treat moderate to severe acne. It is well known that isotretinoin can cause an elevation in liver enzymes, triglycerides, and cholesterol. Laboratory monitoring is indicated while patients are on isotretinoin, but the frequency of laboratory monitoring is very variable among physicians who prescribe it. Study objectivesThis study aimed to determine the frequency of laboratory abnormalities of triglycerides, cholesterol, and liver aminotransferases in acne patients treated with oral isotretinoin in order to assess the need for frequent laboratory monitoring while on isotretinoin and to study the association between body weight and laboratory abnormalities. MethodsA retrospective chart review has been conducted using data extracted from electronic medical records of the

I would like to express my sincere gratitude to my supervisor Prof. Frank Coolen for the continuous support of my Ph.D study and research, for his motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better supervisor and mentor for my Ph.D study. Many thanks to the Durham University. I learned a lot from the seminars organised and the training courses. I would like to thank the sponsors and supporters of my study, Saudi Arabian Cultural Bureau in London, the Ministry of Higher Education in Saudi Arabia and Qassim University. I am truly grateful to my husband Abdullah Alabdulatif for his belief in me and unlimited support. To my two lovely children, Abdulrahman and Mela, who are the pride and joy of my life. Special thanks to my father who has been a great source of motivation and support and my mother for all her unlimited love and prayers and all my brothers and sisters for encouraging and believing. v

In this article, a new family of bivariate discrete distributions is proposed based on the copula concept, in the so-called bivariate discrete odd generalized exponential-G family. Some distributional properties, including the joint probability mass function, joint survival function, joint failure rate function, median correlation coefficient, and conditional expectation, are derived. After proposing the general class, one special model of the new bivariate family is discussed in detail. The maximum likelihood approach is utilized to estimate the family parameters. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood estimators. Finally, the importance of the new bivariate family is explained by means of two distinctive real data sets in various fields.