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Who does not get screened? A simple model of the complex relationships in mammogram non-attendance

Bibliographic Data

ID4227478
AuthorsCelia K Naivar Sen (0000-0002-7791-1422), Celia Kn Sen (Koç University), G Tarcan Kumkale (0000-0002-3638-7118, Koç University)
Year2016
Volume21
Issue12
Pages2838-2850
Publication date2016-12-01
Open AccessYes
TypeARTICLE
VenueJournal of Health Psychology (JOURNAL)
Journal identifiersISSN: 1359-1053 • E-ISSN: 1461-7277
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/1359105315587138
PMID26063211
OpenAlexW1571412831
LanguageEN
Citations received3
References cited33

With increasing mammogram rates, identifying attributes of non-attending women entails going beyond differences in demographic groups to reveal complex interactions among personality attributes. In this study, we analyzed survey data from 474 women aged 41 years and older using decision trees. By incorporating personality, religiousness, and age, we were able to correctly classify 42.9 percent of non-attenders compared to 4.4 percent with logistic regression analysis. Our findings suggest that incorporating personality and religiousness attributes may increase non-attender identification. Furthermore, the simple profile generated by decision trees provides a clear map useful for intervention planning

Attendance · Decision tree · Intervention (counseling · Logistic regression · Machine learning · Personality · Psychiatry · Computer Science · Global Cancer Incidence and Screening · Health disparities and outcomes · Patient-Provider Communication in Healthcare · Psychology · Artificial Intelligence · Clinical Psychology · Social Psychology

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Citations per year0.38
Citation span2018 - 2024 (7)
Highly citedNo

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