Nursing assessment

The purpose of this study was to describe the lived experience of nurses who care for hospitalized women outside of an addiction treatment setting who have a problem with drugs and/or alcohol. The relational experiences of ten registered nurses who had cared for women with drug and alcohol problems were elicited. Heideggerian hermeneutic phenomenology was the method used to interpret the nurse participant's meaning of their experience. The theoretical framework that was used to explore the nurses' experience o caring for women who abuse or are dependent on alcohol and/or drugs was Boykin and Schoenhofer's Nursing as Caring (1993). The relational themes that emerged were: Caring in the dark; Intentionally knowing the woman with AOD as a unique person; and Experiencing sisterhood.

Patients and nurses have been found to perceive support differently. This exploratory study was undertaken to: (a) identify those nursing behaviors perceived by emergency department patients as supportive, and (b) to identify the importance emergency room patients give to various nursing behaviors on a checklist. A sample of 30 emergency department patients completed a guided interview and the Supportive Nursing Behavior Checklist. The perceived attitude of the nurse toward the patient and the availability of the nurse to the patient were the most important factors influencing patients' perceptions of support. The nurse being friendly and cheerful were the most important behaviors. The study has implications for nursing practice, nursing administration and nursing education. Suggestions are also given for further research.

This research study is a description of the experience of receiving nursing care for homeless mothers. Nine homeless mothers, who described their experience of receiving nursing care, were selected as participants. Using a phenomenological approach to guide the inquiry, narrative descriptions were generated from interviews with each participant. The homeless mothers' experience of receiving nursing care is described in two essential descriptive themes: (1) being homeless, and (2) receiving nursing care. The implications of these mothers' experience for nursing education, practice, and research are discussed.

Health care systems consist of various individuals and organizations that aim to meet the health care needs of people and provide a complete and responsive health care solution. One of the important aspects of a health care delivery system is nursing. The use of technology is a vital aspect for delivering an optimum and complete nursing care to individuals; and also for improving the quality and delivery mechanism of nursing care. The model proposed in this thesis for Nursing Knowledge Management System is a novel knowledge-based decision support system for nurses to capture and manage nursing practice, and further, to monitor nursing care quality, as well as to test aspects of an electronic health record for recording and reporting nursing practice. As a part of a collaborative research of the Christine E. Lynn College of Nursing and the Department of Computer Science, a prototype toolset was developed to capture and manage nursing practice in order to improve the quality of care. This thesis focuses on implementing a web based SOA solution for Automated Classification of Nursing Care Categories, based on the knowledge gained from the prototype for nursing care practice.

Provision of complete and responsive solution to healthcare services requires a multi-tired health delivery system. One of the aspects of healthcare hierarchy is the need for nursing care of the patient. Nursing care and observation provide basis for nurses to communicate with other aspects of healthcare system. The ability of capturing and managing nursing practice is essential to the quality of human care. The thesis proposes knowledge based decision making and analyzing system for the nurses to capture and manage the nursing practice. Moreover it allows them to monitor nursing care quality, as well as to test an aspect of an electronic healthcare record for recording and reporting nursing practice. The framework used for this system is based on nursing theory and is coupled with the quantitative analysis of qualitative data. It allows us to quantify the qualitative raw natural nursing language data. The results are summarized in the graph that shows the relative importance of those attributes with respect to each other at different instances of nurse-patient encounter. Research has been conducted by the Department of Computer and Electrical Engineering and Computer Science for the College of Nursing.

Promoting healthcare and wellbeing requires the dedication of a multi-tiered health service delivery system, which is comprised of specialists, medical doctors and nurses. A holistic view to a patient care perspective involves emotional, mental and physical healthcare needs, in which caring is understood as the essence of nursing. Properly and efficiently capturing and managing nursing knowledge is essential to advocating health promotion and illness prevention. This thesis proposes a document-indexing framework for automating classification of nursing knowledge based on nursing theory and practice model. The documents defining the numerous categories in nursing care model are structured with the help of expert nurse practitioners and professionals. These documents are indexed and used as a benchmark for the process of automatic mapping of each expression in the assessment form of a patient to the corresponding category in the nursing theory model. As an illustration of the proposed methodology, a prototype application is developed using the Latent Semantic Indexing (LSI) technique. The prototype application is tested in a nursing practice environment to validate the accuracy of the proposed algorithm. The simulation results are also compared with an application using Lucene indexing technique that internally uses modified vector space model for indexing. The result comparison showed that the LSI strategy gives 87.5% accurate results compared to the Lucene indexing technique that gives 80% accuracy. Both indexing methods maintain 100% consistency in the results.