Implementation and Evaluation of the Quantification of Blood Loss and Postpartum Hemorrhage Education

Postpartum hemorrhage (PPH) is an obstetric emergency that can lead to maternal morbidly and mortality. The literature consistently shows that numerous maternal deaths related to PPH are preventable. The significant contributors identified to maternal deaths from PPH include deficient education obstetrical nurses have on PPH, delayed recognition, and miscalculations of blood loss. After completing a literature review on the current PPH practice and determining the current method of estimation of blood loss (EBL) to be inaccurate, obstetric nurses at a rural, southeast Kansas hospital were educated on PPH to improve recognition and determine cumulative blood loss more accurately by the method of quantification. This study utilized a one-group pretest-posttest design to determine the knowledge gained on PPH and the technique of quantifying blood loss (QBL) after an educational program. A PPH cart was created and navigated to help the nurses calculate QBL and prevent delays in PPH management. The nurses completed a six-week postimplementation survey to determine how the education program affected their knowledge and skills regarding PPH, QBL, and the PPH cart. According to the findings, the study indicated the education program over PPH improved PPH knowledge and skills to quantify blood loss. The results determined the PPH cart was beneficial in performing QBL and the management of PPH. In conclusion, the educational program and conversion from estimation to the quantification of blood loss could ultimately decrease maternal morbidity and mortality

Teaching About Criminal Victimization: Guidelines for Faculty

These guidelines are designed to provide support to college and university faculty in teaching about criminal victimization, regardless of the course or discipline the material will be presented in

Responding to Victims of Crime: Basics for Interns

This curriculum kit provides college students an introduction to basic skills for interacting with victims of crime in an internship or field placement setting

Providing Care and Support for Victims of Crime: Exercises and Assignments

Exercises and assignments created to provide college students with a foundation on how to be present for and provide resources to people in their lives who are victimized by crime, including those who share their campus environment

The i* framework for goal-oriented modeling

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39417-6i* is a widespread framework in the software engineering field that supports goal-oriented modeling of socio-technical systems and organizations. At its heart lies a language offering concepts such as actor, dependency, goal and decomposition. i* models resemble a network of interconnected, autonomous, collaborative and dependable strategic actors. Around this language, several analysis techniques have emerged, e.g. goal satisfaction analysis and metrics computation. In this work, we present a consolidated version of the i* language based on the most adopted versions of the language. We define the main constructs of the language and we articulate them in the form of a metamodel. Then, we implement this version and a concrete technique, goal satisfaction analys is based on goal propagation, using ADOxx. Throughout the chapter, we used an example based on open source software adoption to illustrate the concepts and test the implementation.Peer ReviewedPostprint (author's final draft

Towards iStarML 2.0: Closing gaps from evolved requirements

iStarML is an XML-based format for enabling interoperability among i* tools. Its main design focus was to support data interchange even when involved tools implement different i* variants. In this paper we analyse required changes to the format from two main sources (i) the evolution of i* into a consistent and clear set of core concepts expressed in the new iStar 2.0 specification and (ii) recurrent necessities due to a wide use of i* modelling. In order to address these requirements, we propose new XML elements to be considered in a new version of iStarML: iStarML2.0Peer ReviewedPostprint (published version

Towards a framework for improving goal-oriented requirement models quality

Goal-orientation is a widespread and useful approach to Requirements Engineering. However, quality assessment frameworks focused on goal-oriented processes are either limited or remain on the theoretical side. Requirements quality initiatives range from simple metrics applicable to requirements documents, to general-purpose quality frameworks that include syntactic, semantic and pragmatic concerns. In some recent works, we have proposed a metrics framework for goal-oriented models, but the approach did not cover the cycle of quality assessment. In this paper we present a semiotic-based quality assessment proposal built upon the i* framework and the SEQUAL proposal. We propose a simplification of SEQUAL which can be applied to i* models by defining semantic, pragmatic and social metrics. As a result, we obtain suites of metrics that can be applied to i* goal-oriented requirements models. This theoretical work is put into practice by using iStarML, a XML representation of i* models, over which XQuery sentences compute the proposed metrics.Peer ReviewedPostprint (published version