2.50
Hdl Handle:
http://hdl.handle.net/10755/165265
Category:
Abstract
Type:
Presentation
Title:
THE USE OF CLUSTER ANALYSIS TO STUDY CANCER GENETICS COMMUNICATION
Author(s):
Ellington, Lee; Bjerregaard-Petersen, Stefanie; Dudley, William; Baty, Bonnie; Smith, Ken; Botkin, Jeffrey
Author Details:
Lee Ellington, University of Utah, College of Nursing, Salt Lake City, Utah, USA; Stefanie Bjerregaard-Petersen; William Dudley, PhD; Bonnie Baty, MS; Ken Smith, PhD; Jeffrey Botkin MD, MPH
Abstract:
Genetic testing for cancer-susceptibility gene mutations helps at-risk individuals make informed decisions about their health and the health of their family. As genetic technology rapidly evolves and as the public increasingly becomes its consumer, we know little about what takes place in genetic counseling sessions---it’s a virtual “black box.” The study purpose is to: 1) adapt a medical coding system to genetic communication; 2) identify patterns of communication, and 3) examine predictors of communication. The model of “relationship-centered care” (Tresolini, 1994) provides the study framework. According to this model, the foundation of a therapeutic relationship is the integration and synthesis of both the patient and provider perspective and is reflected in their dialogue. The 167 participants are members of the BRCA1 K2082. Three genetic counselors (GC) conducted sessions according to a research protocol. Roter's Interactional Analysis System (RIAS) was used to code every utterance of audiotaped pre-test sessions. RIAS categories were combined to create constructs which reflect session content and processes. Four analytic methods were used: descriptive statistics, Pearson coefficients, hierarchical cluster analysis, and chi-square. Most participants were married (83.8%), female (56.9%), and the mean age was 47.6 years. 26% were carriers, 59.9% were non-carriers, and 13.8% did not learn results. Coder reliability was calculated (r > .87). GCs were verbally dominant, making 70.3% of the statements, clients made 25.3%, and significant others accounted for 4.4%. Using combined RIAS categories as input variables, cluster analytic methods identified four patterns of communication: GC Driven/Educational (n=45); Interactive with Medical Focus (n=47); Moderately Interactive (n=42); and Client-Centered (n=33). Surprisingly, GC significantly predicted cluster differences (p < .0001); whereas, client characteristics did not (gender, marital status, and cancer history, p’s > .20). Findings indicate that a widely used physician/patient coding system which has been predominantly used in primary care settings can be successfully adapted to cancer genetic counseling encounters. Despite use of research protocols to standardize sessions, communication patterns were driven by GC style and did not vary according to client characteristics. The cluster analytic method shows promise in identifying unique communication patterns and associations with client and provider variables.
Repository Posting Date:
27-Oct-2011
Date of Publication:
27-Oct-2011
Conference Date:
2005
Conference Name:
30th Annual Oncology Nursing Society Congress
Conference Host:
Oncology Nursing Society
Conference Location:
Orlando, Florida, USA
Sponsors:
Funding Sources: R03 HG02359
Note:
This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.

Full metadata record

DC FieldValue Language
dc.type.categoryAbstracten_US
dc.typePresentationen_GB
dc.titleTHE USE OF CLUSTER ANALYSIS TO STUDY CANCER GENETICS COMMUNICATIONen_GB
dc.contributor.authorEllington, Leeen_US
dc.contributor.authorBjerregaard-Petersen, Stefanieen_US
dc.contributor.authorDudley, Williamen_US
dc.contributor.authorBaty, Bonnieen_US
dc.contributor.authorSmith, Kenen_US
dc.contributor.authorBotkin, Jeffreyen_US
dc.author.detailsLee Ellington, University of Utah, College of Nursing, Salt Lake City, Utah, USA; Stefanie Bjerregaard-Petersen; William Dudley, PhD; Bonnie Baty, MS; Ken Smith, PhD; Jeffrey Botkin MD, MPHen_US
dc.identifier.urihttp://hdl.handle.net/10755/165265-
dc.description.abstractGenetic testing for cancer-susceptibility gene mutations helps at-risk individuals make informed decisions about their health and the health of their family. As genetic technology rapidly evolves and as the public increasingly becomes its consumer, we know little about what takes place in genetic counseling sessions---it&rsquo;s a virtual &ldquo;black box.&rdquo; The study purpose is to: 1) adapt a medical coding system to genetic communication; 2) identify patterns of communication, and 3) examine predictors of communication. The model of &ldquo;relationship-centered care&rdquo; (Tresolini, 1994) provides the study framework. According to this model, the foundation of a therapeutic relationship is the integration and synthesis of both the patient and provider perspective and is reflected in their dialogue. The 167 participants are members of the BRCA1 K2082. Three genetic counselors (GC) conducted sessions according to a research protocol. Roter's Interactional Analysis System (RIAS) was used to code every utterance of audiotaped pre-test sessions. RIAS categories were combined to create constructs which reflect session content and processes. Four analytic methods were used: descriptive statistics, Pearson coefficients, hierarchical cluster analysis, and chi-square. Most participants were married (83.8%), female (56.9%), and the mean age was 47.6 years. 26% were carriers, 59.9% were non-carriers, and 13.8% did not learn results. Coder reliability was calculated (r &gt; .87). GCs were verbally dominant, making 70.3% of the statements, clients made 25.3%, and significant others accounted for 4.4%. Using combined RIAS categories as input variables, cluster analytic methods identified four patterns of communication: GC Driven/Educational (n=45); Interactive with Medical Focus (n=47); Moderately Interactive (n=42); and Client-Centered (n=33). Surprisingly, GC significantly predicted cluster differences (p &lt; .0001); whereas, client characteristics did not (gender, marital status, and cancer history, p&rsquo;s &gt; .20). Findings indicate that a widely used physician/patient coding system which has been predominantly used in primary care settings can be successfully adapted to cancer genetic counseling encounters. Despite use of research protocols to standardize sessions, communication patterns were driven by GC style and did not vary according to client characteristics. The cluster analytic method shows promise in identifying unique communication patterns and associations with client and provider variables.en_GB
dc.date.available2011-10-27T12:15:27Z-
dc.date.issued2011-10-27en_GB
dc.date.accessioned2011-10-27T12:15:27Z-
dc.conference.date2005en_US
dc.conference.name30th Annual Oncology Nursing Society Congressen_US
dc.conference.hostOncology Nursing Societyen_US
dc.conference.locationOrlando, Florida, USAen_US
dc.description.sponsorshipFunding Sources: R03 HG02359-
dc.description.noteThis is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item. If a citation is listed in this record, the item has been published and is available via open-access avenues or a journal/database subscription. Contact your library for assistance in obtaining the as-published article.-
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