2.50
Hdl Handle:
http://hdl.handle.net/10755/157637
Type:
Presentation
Title:
Symposium Overview Abstract: Session 1120
Abstract:
Symposium Overview Abstract: Session 1120
Conference Sponsor:Western Institute of Nursing
Conference Year:2009
Author:Sousa, Karen H., RN, PhD
P.I. Institution Name:University of Colorado Denver, College of Nursing
Title:Professor
Contact Address:13120 East 19th Ave., Denver, CO, 80045, USA
Contact Telephone:720-854-8883
Symptoms are key in the health-illness experience, are multidimensional, and parallel the multidimensional models of patient care that shape clinical nursing care. An important barrier to symptom management is the lack of research describing symptoms, their patterns of association, change over time, interactions, and synergy. The evaluation of symptom clusters has been described as a means to in-depth understanding between and among symptoms and to provide new avenues for intervention research that reduces the impact of symptoms on health-related outcomes. New models and approaches for the analysis of symptom clusters will be explored in this symposium. The challenges addressed are the ability to maximize access to homogeneous groups of individuals with regards to the symptoms of interest, modeling approaches to explore and confirm the structure of clusters, and understanding changes in clusters over time. The symposium will foster dialog specific to differing methodological perspectives and discuss innovative approaches to understand symptom clusters. The data used for each paper include responses to surveys that were collected as part of the AIDS Time-Oriented Health Outcome Study (ATHOS) databank. The ATHOS is a longitudinal database for persons with HIV-associated illness developed through a grant from the Agency for Health Care Policy and Research (HSO6211, J. F. Fries, PI). The sample consists of 917 men, 83% of whom were Caucasian. The symptoms included represent 23 symptoms from the Sign and Symptom Checklist for Persons with HIV Disease developed by Holzemer and colleagues. Dr. Matthews will discuss an approach for randomizing individuals into research studies that maximizes the likelihood that groups are balanced with regards to important factors such as symptom clusters of interest. She will introduce and discuss Urn Randomization as an approach to promote balance between groups, particularly as it relates to the symptom clusters of interest. The most common approaches to grouping symptoms into clusters are exploratory factor analysis and cluster analysis. Dr. Meek will compare and discuss the different structures obtained based on the two approaches and point out variations in assumptions and techniques. In addition, she will suggest guidelines that can inform an analysis strategy aimed at cluster formation. Structural Equation Modeling is a statistical methodology to confirm that measurement models reflect symptom clusters as they are truly experienced by individuals, and provides an opportunity for nurse researchers and clinicians to more appropriately evaluate patient outcomes. Dr. Sousa will discuss symptom cluster analysis from a Structural Equation Modeling perspective. Understanding symptom clusters? change over time is imperative to developing appropriate interventions and understanding disease progression. Dr. Cook will demonstrate Hierarchical Linear Modeling?s utility in analyzing symptom clusters across time. He will discuss how this technique helps to understand the relationship between symptom clusters and disease progression.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleSymposium Overview Abstract: Session 1120en_GB
dc.identifier.urihttp://hdl.handle.net/10755/157637-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Symposium Overview Abstract: Session 1120</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Sousa, Karen H., RN, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Colorado Denver, College of Nursing</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">13120 East 19th Ave., Denver, CO, 80045, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">720-854-8883</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">karen.sousa@ucdenver.edu</td></tr><tr><td colspan="2" class="item-abstract">Symptoms are key in the health-illness experience, are multidimensional, and parallel the multidimensional models of patient care that shape clinical nursing care. An important barrier to symptom management is the lack of research describing symptoms, their patterns of association, change over time, interactions, and synergy. The evaluation of symptom clusters has been described as a means to in-depth understanding between and among symptoms and to provide new avenues for intervention research that reduces the impact of symptoms on health-related outcomes. New models and approaches for the analysis of symptom clusters will be explored in this symposium. The challenges addressed are the ability to maximize access to homogeneous groups of individuals with regards to the symptoms of interest, modeling approaches to explore and confirm the structure of clusters, and understanding changes in clusters over time. The symposium will foster dialog specific to differing methodological perspectives and discuss innovative approaches to understand symptom clusters. The data used for each paper include responses to surveys that were collected as part of the AIDS Time-Oriented Health Outcome Study (ATHOS) databank. The ATHOS is a longitudinal database for persons with HIV-associated illness developed through a grant from the Agency for Health Care Policy and Research (HSO6211, J. F. Fries, PI). The sample consists of 917 men, 83% of whom were Caucasian. The symptoms included represent 23 symptoms from the Sign and Symptom Checklist for Persons with HIV Disease developed by Holzemer and colleagues. Dr. Matthews will discuss an approach for randomizing individuals into research studies that maximizes the likelihood that groups are balanced with regards to important factors such as symptom clusters of interest. She will introduce and discuss Urn Randomization as an approach to promote balance between groups, particularly as it relates to the symptom clusters of interest. The most common approaches to grouping symptoms into clusters are exploratory factor analysis and cluster analysis. Dr. Meek will compare and discuss the different structures obtained based on the two approaches and point out variations in assumptions and techniques. In addition, she will suggest guidelines that can inform an analysis strategy aimed at cluster formation. Structural Equation Modeling is a statistical methodology to confirm that measurement models reflect symptom clusters as they are truly experienced by individuals, and provides an opportunity for nurse researchers and clinicians to more appropriately evaluate patient outcomes. Dr. Sousa will discuss symptom cluster analysis from a Structural Equation Modeling perspective. Understanding symptom clusters? change over time is imperative to developing appropriate interventions and understanding disease progression. Dr. Cook will demonstrate Hierarchical Linear Modeling?s utility in analyzing symptom clusters across time. He will discuss how this technique helps to understand the relationship between symptom clusters and disease progression.</td></tr></table>en_GB
dc.date.available2011-10-26T20:03:38Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:03:38Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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