A Practical Understanding of Preeclampsia for a Nurse in a Third World Setting

Preeclampsia is a disease of pregnancy that affects approximately 3-5% of women with child. It is one of the primary causes of mortality in mothers and babies across the globe. The exact cause, pathogenesis, or disease progression is unknown. Therefore, there is no definition of which patients are at risk for developing preeclampsia and what can work as a preventative measure. In high socioeconomic settings where there is good healthcare, standard treatment is established to manage the symptoms and decrease the progression of preeclampsia to eclampsia. However, in more rural, third-world settings of developing countries, caring for patients with preeclampsia is not a straightforward matter. Due to decreased access to health care, low economic status, and lack of education, preeclampsia is often seen yet seldom treated among this population. The discussion below addresses several possible pathophysiological processes of preeclampsia, as well as potential risk factors. The standard treatments of care are then discussed, followed by the evaluation of studies regarding alternative treatments for preeclampsia. The importance of screening pregnant women in developing nations is included. The discussion is concluded by a summary of what caring for preeclampsia in a third-world setting might look like for a missionary nurse

Conversation analysis at work: detection of conflict in competitive discussions through semi-automatic turn-organization analysis

This study proposes a semi-automatic approach aimed at detecting conflict in conversations. The approach is based on statistical techniques capable of identifying turn-organization regularities associated with conflict. The only manual step of the process is the segmentation of the conversations into turns (time intervals during which only one person talks) and overlapping speech segments (time intervals during which several persons talk at the same time). The rest of the process takes place automatically and the results show that conflictual exchanges can be detected with Precision and Recall around 70% (the experiments have been performed over 6 h of political debates). The approach brings two main benefits: the first is the possibility of analyzing potentially large amounts of conversational data with a limited effort, the second is that the model parameters provide indications on what turn-regularities are most likely to account for the presence of conflict