2.50
Hdl Handle:
http://hdl.handle.net/10755/158686
Type:
Presentation
Title:
Generating Scientific Models of Knowledge Using Arcs©
Abstract:
Generating Scientific Models of Knowledge Using Arcs©
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2004
Author:Kim, Jinshil, MSN, RN
P.I. Institution Name:IUPUI
Contact Address:IUPUI, Adult Health, 1111 Middle Drive, Indianapolis, IN, 46202, USA
The continual increase of research provides valuable information that requires substantial skill and time to search and evaluate the relevant studies (Brown, 1994). Numerous studies of nutrition in heart failure (HF) need to be reviewed for their scientific merit, relevance, and usefulness in an effective manner. However, few authors have attempted to summarize the literature using a systematic approach with an exception of meta-analysis. Purpose: The purposes of the study were to (a) conduct an integrative review of 10 data-based studies of nutrition in HF, using arcs©, a computer system for managing and modeling empirical knowledge, and (b) generate scientific models of knowledge to determine the feasibility of arcs©. Method: Arcs© identifies concepts of the knowledge that can be stored in data files. This is different from the meta-analysis that provides a statistical synthesis of knowledge. One of the unique features of arcs© is that it provides scientific models for empirical testing. Results: All 10 studies were explanatory observational designs, including tables and graphs by which main results were reported. Arcs© aggregated studies by variables and relationships among variables: 104 dependent and 93 independent operational variables; 19 dependent and 22 independent abstract variables; and 59 associational, 16 predictive, 14 structural, and 1 descriptive relationships, and 85 differences. A direct model produced by arcs© demonstrated a structural relationship of 18-month mortality and cachexia in HF, independent of age, NYHA, peak VO2, exercise time, and Na+. The model can be tested as a path-theoretical model. Conclusion: Arcs© appeared to be feasible to conduct an integrative review of nutrition in HF. A representative set of literature will enable me to generate the full knowledgebase for the domain of nutrition in HF and decide gaps and conflicts in knowledge.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleGenerating Scientific Models of Knowledge Using Arcs©en_GB
dc.identifier.urihttp://hdl.handle.net/10755/158686-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Generating Scientific Models of Knowledge Using Arcs&copy;</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2004</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Kim, Jinshil, MSN, RN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">IUPUI</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">IUPUI, Adult Health, 1111 Middle Drive, Indianapolis, IN, 46202, USA</td></tr><tr><td colspan="2" class="item-abstract">The continual increase of research provides valuable information that requires substantial skill and time to search and evaluate the relevant studies (Brown, 1994). Numerous studies of nutrition in heart failure (HF) need to be reviewed for their scientific merit, relevance, and usefulness in an effective manner. However, few authors have attempted to summarize the literature using a systematic approach with an exception of meta-analysis. Purpose: The purposes of the study were to (a) conduct an integrative review of 10 data-based studies of nutrition in HF, using arcs&copy;, a computer system for managing and modeling empirical knowledge, and (b) generate scientific models of knowledge to determine the feasibility of arcs&copy;. Method: Arcs&copy; identifies concepts of the knowledge that can be stored in data files. This is different from the meta-analysis that provides a statistical synthesis of knowledge. One of the unique features of arcs&copy; is that it provides scientific models for empirical testing. Results: All 10 studies were explanatory observational designs, including tables and graphs by which main results were reported. Arcs&copy; aggregated studies by variables and relationships among variables: 104 dependent and 93 independent operational variables; 19 dependent and 22 independent abstract variables; and 59 associational, 16 predictive, 14 structural, and 1 descriptive relationships, and 85 differences. A direct model produced by arcs&copy; demonstrated a structural relationship of 18-month mortality and cachexia in HF, independent of age, NYHA, peak VO2, exercise time, and Na+. The model can be tested as a path-theoretical model. Conclusion: Arcs&copy; appeared to be feasible to conduct an integrative review of nutrition in HF. A representative set of literature will enable me to generate the full knowledgebase for the domain of nutrition in HF and decide gaps and conflicts in knowledge. </td></tr></table>en_GB
dc.date.available2011-10-26T21:17:53Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T21:17:53Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
All Items in this repository are protected by copyright, with all rights reserved, unless otherwise indicated.