Demystifying writing papers for biomedical journals

This paper attempts to demystify the publication process in biomedical journals by offering simple step by step recipes on how to write and get published. The author relies on a narrative review and personal experience as a fellow student of publishing to achieve objective. Five stages in the publication process were identified: Planning, Writing, Submission, Managing editorial decisions and Post-acceptance. Planning is probably the most neglected yet the most important stage. The author begins by making sure that all necessary resources are in place, develops careful outline, decides on authorship and selects potential journal for publication. The paper will typically have three sections. The title which contains title of the paper and the list of authors and also key words is the first section. The abstract and the body of the paper are also the second and third sections. The title and abstract are critical and are usually revised many more times than the other parts of the paper. Choosing key words carefully increases the chance of the paper being read and cited. The body of the paper follows what is called the Introduction, Methods, Results and Discussion structure. Submission begins by writing a cover letter that makes the case for the article. Following all the key steps of the submission process avoids early rejection. Respond speedily and graciously and in sufficient detail to reviewers' and editors' comments. Following publication,the author should ensure that the published knowledge is disseminated widely. Overall, writing for biomedical journals is not too mysterious. However, there is no short cut: learning to write successfully takes time and practice

EMJ series on statistics and methods: variables, populations and samples

This the first article in the EMJ Series on Statistics and Methods describes "variables" which represent different demographic and clinical characteristics of several individuals in a data set. Various attributes of quantitative and qualitative variables, including type, scale of measurement and values they may take are presented. Some descriptive statistics and graphic summaries are introduced. Pertinent examples drawn from journal articles are provided. The article also defines "population" as used in statistics and epidemiology and highlights population in size and scope in the context of the research question to be answered. It emphasizes the need for explicitly defining sampling based on the research question in a particular study. In addition, the articles introduces concepts and notations related to parameter and statistic. It highlights the need for using appropriate sampling method in selecting a representative sample of the study population and to be able to make valid inferences and generalizations. It is indicated in this articles that specific issues requiring further details will be addressed in the Series articles in subsequent Issues of EMJ

EMJ series on statistics and methods: normal distribution and the central limit theorem

This second article in the EMJ Series on Statistics and Methods dwells on the basics of sampling distribution of' variables', which are presented in detail in the preceding article in this Issue of the Ethiopian Medical Journal (EMJ). The present article highlights recommended routines that need to be undertaken in order to understand information collected in a particular study before embarking on doing complex statistical analyses. It underscores the importance of descriptive statistics as a means to getting insights into data quality and learn about the scale and distribution of different variables in a data set. The article emphasizes the need for assessing the sampling distributions of variables as a prerequisite to making decide on selection of appropriate statistical techniques for in a data set. It describes salient features of a normally distributed random variable and touches on some other probability distributions commonly used in epidemiological studies. The article also describes the central limit theorem highlighting salient points on its conceptual basis in understanding sampling distributions of sample means and the implications of using normal distribution to make inference about the population based on summary measures from a sufficiently large sample