<p>Researchers from different disciplines bring to a project their different perspectives of the research problem. Differences in education, experiences, and understanding create a research project that has more depth and breadth than one designed by researchers from a single discipline. The purpose of this article is to describe how faculty from two disciplines, nursing and education, used the interpretive narrative method, a qualitative research method, in a pilot project to examine issues related to the recruitment and retention of Hispanic nurses. The researchers chose the interpretive narrative method for the flexibility it offers interdisciplinary research, its power in eliciting comprehensive narratives from participants, and the possibilities it offers for analysis. </p> <h4>AUTHORS</h4> <p>Received: October 23, 2004</p> <p>Accepted: April 1, 2005</p> <p>Dr. McQueen is Assistant Professor of Nursing, North Carolina A&T State University, School of Nursing, Greensboro, North Carolina, and Dr. Zimmerman is Associate Professor, Education, Purdue University Calumet, Hammond, Indiana.</p> <p>Address correspondence to Laura McQueen, PhD, APRN, BC, Assistant Professor of Nursing, North Carolina A&T State University, School of Nursing, 1601 East Market Street, Noble Hall, Greensboro, NC 27411; e-mail: <a href="mailto:llmcquee@ncat.edu" shape="rect">llmcquee@ncat.edu</a>.</p>
Compassion fatigue is an unrecognized and understudied phenomenon that occurs in practicing nurses and most likely to affect nurse educators in practice. This unique form of burnout can impede nurse educators in caring for themselves and their students, patients, colleagues, schools and healthcare agencies. It is important to recognize the outcomes and implications of compassion fatigue and the growing need to mentor and care for both current and future nurse educators. Further research studies are needed on compassion fatigue in nurse educators to minimize the impact on educator health and professional nursing care.
Background and Objectives: In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS. In the absence of real-world testing data and models, which require a substantial investment, we provide a heuristic assessment of the feasibility of automatically populating QSRS questions from EHR data. Methods:To provide an assessment of the automation feasibility for QSRS, we first developed a heuristic framework, the Relative Abstraction Complexity Framework, to assess relative complexity of data abstraction questions. This framework assesses the relative complexity of characteristics or features of abstraction questions that should be considered when determining the feasibility of automating QSRS. Questions are assigned a final relative complexity score (RCS) of low, medium, or high by a team of clinicians, human factors, and natural language processing researchers.Results: One hundred thirty-four QSRS questions were coded using this framework by a team of natural language processing and clinical experts. Fifty-five questions (41%) had high RCS and would be more difficult to automate, such as "Was use of a device associated with an adverse outcome(s)?" Forty-two questions (31%) had medium RCS, such as "Were there any injuries as a result of the fall(s)?" and 37 questions (28%) had low RCS, such as "Did the patient deliver during this stay?" These results suggest that Blood and Hospital Acquired Infections-Clostridium Difficile Infection (HAI-CDI) modules would be relatively easier to automate, whereas Surgery and HAI-Surgical Site Infection would be more difficult to automate. Conclusions:Although EHRs contain a wealth of information, abstracting information from these records is still very challenging, particularly for complex questions, such as those concerning patient adverse events. In this work, we developed a heuristic framework, which can be applied to help guide conversations around the feasibility of automating QSRS data abstraction. This framework does not aim to replace testing with real data but complement the process by providing initial guidance and direction to subject matter experts to help prioritize, which abstraction questions to test for feasibility using real data.
This study examines a group of middle school students who are Hispanic and their interest in attending college and pursuing a career in nursing. Although males and females both participated, mostly females expressed an interest in nursing as a profession. In this study investigators examined Hispanic middle school students' knowledge and interest in attending college for a nursing degree and discussed methods to assist in developing more interest in nursing as a career choice for middle school students who are Hispanic. This project supports the need for creating a diverse nursing workforce through inclusion of underrepresented populations. The significance of this study is that it can be used to develop programs aimed at recruitment and retention of student nurses who are Hispanic.
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