Reliability of Real-Time Coding for Post-MI Attributions: Intervention Implications

2.50
Hdl Handle:
http://hdl.handle.net/10755/160813
Type:
Presentation
Title:
Reliability of Real-Time Coding for Post-MI Attributions: Intervention Implications
Abstract:
Reliability of Real-Time Coding for Post-MI Attributions: Intervention Implications
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2009
Author:Messinger, Cathy, MSN
P.I. Institution Name:University of Iowa
Contact Address:200 Hawkins Drive, T407 GH, Iowa City, IA, 52242-1009, USA
Contact Telephone:319-384-8350
Co-Authors:C.J. Messinger, College of Nursing, University of Iowa, Iowa City, IA; R. Martin, M. Kilburg, H. Schacht Reisinger, Center for Research in the Implementation of Innovative Strategies in Practice (CRIISP), VA Medical Center, Iowa City, IA;
Conceptual Framework: Illness representations regulate self-care behaviors. Post-myocardial infarction (MI) representations often fail to incorporate risk factors; these beliefs predict non-adherence behaviors. Interventions to modify representations might facilitate post-MI lifestyle change. However, optimal assessment of attributions requires a narrative interview and the time-consuming process of content analysis is a barrier to the development of such interventions for clinical settings. Purpose: To determine whether interviewers could reliably code post-MI illness attributions in real-time. Subjects: Post-MI patients were recruited from VA Medical Centers as part of a larger illness representations study; data from the first 15 were included in this evaluation. Method: During semi-structured, audio-recorded telephone interviews, trained interviewers asked participants, "What sort of factors do you think contributed to your heart attack?" Immediately afterwards, interviewers coded whether (yes/no) participants had made attributions to each of 18 categories. Audio-recordings were transcribed verbatim. Transcripts were content-analyzed by two independent coders, using the same categories. Categories (e.g., stress, diet) were defined on the basis of the literature; interviewers and coders received identical training. Results: First, inter-rater reliability was evaluated between coders. Cronbach's alpha for each category was satisfactory (> .70). Disagreements were discussed to consensus, producing composite codes for each transcript. Next, reliability was checked between the composite codes and interviewers. Satisfactory agreement (alpha > .70) was observed for 15 categories. Low agreement was seen for three categories, including: (1) expression of uncertainty (.31), (2) personal cardiac history (.43), and (3) physiological phenomena (e.g., blood clot) (.67). Conclusions: Findings suggest that most categories of MI attributions can be reliably coded by interviewers in real-time. Such an approach is efficient and thus may facilitate the implementation of attributional interventions in clinical practice. Findings were used to refine the variable dictionary and provide interviewer booster training.
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.titleReliability of Real-Time Coding for Post-MI Attributions: Intervention Implicationsen_GB
dc.identifier.urihttp://hdl.handle.net/10755/160813-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Reliability of Real-Time Coding for Post-MI Attributions: Intervention Implications</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">2009</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Messinger, Cathy, MSN</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Iowa</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">200 Hawkins Drive, T407 GH, Iowa City, IA, 52242-1009, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">319-384-8350</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">catherine-messinger@uiowa.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">C.J. Messinger, College of Nursing, University of Iowa, Iowa City, IA; R. Martin, M. Kilburg, H. Schacht Reisinger, Center for Research in the Implementation of Innovative Strategies in Practice (CRIISP), VA Medical Center, Iowa City, IA;</td></tr><tr><td colspan="2" class="item-abstract">Conceptual Framework: Illness representations regulate self-care behaviors. Post-myocardial infarction (MI) representations often fail to incorporate risk factors; these beliefs predict non-adherence behaviors. Interventions to modify representations might facilitate post-MI lifestyle change. However, optimal assessment of attributions requires a narrative interview and the time-consuming process of content analysis is a barrier to the development of such interventions for clinical settings. Purpose: To determine whether interviewers could reliably code post-MI illness attributions in real-time. Subjects: Post-MI patients were recruited from VA Medical Centers as part of a larger illness representations study; data from the first 15 were included in this evaluation. Method: During semi-structured, audio-recorded telephone interviews, trained interviewers asked participants, &quot;What sort of factors do you think contributed to your heart attack?&quot; Immediately afterwards, interviewers coded whether (yes/no) participants had made attributions to each of 18 categories. Audio-recordings were transcribed verbatim. Transcripts were content-analyzed by two independent coders, using the same categories. Categories (e.g., stress, diet) were defined on the basis of the literature; interviewers and coders received identical training. Results: First, inter-rater reliability was evaluated between coders. Cronbach's alpha for each category was satisfactory (&gt; .70). Disagreements were discussed to consensus, producing composite codes for each transcript. Next, reliability was checked between the composite codes and interviewers. Satisfactory agreement (alpha &gt; .70) was observed for 15 categories. Low agreement was seen for three categories, including: (1) expression of uncertainty (.31), (2) personal cardiac history (.43), and (3) physiological phenomena (e.g., blood clot) (.67). Conclusions: Findings suggest that most categories of MI attributions can be reliably coded by interviewers in real-time. Such an approach is efficient and thus may facilitate the implementation of attributional interventions in clinical practice. Findings were used to refine the variable dictionary and provide interviewer booster training.</td></tr></table>en_GB
dc.date.available2011-10-26T23:11:05Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T23:11:05Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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