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Development of a Scale to Assess Cancer Attitude in the Community

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info:eu-repo/semantics/closedAccess

Date

2024

Author

Uysal, Nese
Toprak, Filiz Unal

Metadata

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Abstract

Aim: The aim of this study was to develop the Cancer Attitude Scale (CAS) and to evaluate its psychometric properties. Methods: The study was conducted in a mixed-methods study. The preliminary version of the CAS was created by developing an item pool, and qualitative interviews. The items' content validity ratio was calculated to assess content validity. Construct validity was assessed using exploratory factor analysis and confirmatory factor analysis. Internal consistency and test-retest methods were used to determine the reliability of the scale. The Cancer Information Burden Scale was used to evaluate the convergent validity of the scale. Three hundred twenty-five healthy individuals selected by convenience sampling method were included in this study. Results: The CAS sub-dimensions were determined as cancer diagnosis, cancer prevention, stigmatization of individuals with cancer, and personal empowerment after cancer. The content validity of the 22-item scale was 0.94 and four factors explaining 56% of the variance. The model fit indices were x(2)/df = 1.968, RMSEA = 0.055, SRMR = 0.085, and GFI = 0.904.. Cronbach's a and test-retest reliability coefficients of the total scale are 0.76 and 0.936, respectively. The CAS and Cancer Information Overload Scale showed acceptable convergent validity (r = 0.435, P < .001). Conclusions: The Cancer Attitude Scale is a valid and reliable tool that can be applied to assess the attitudes of individuals in the community toward cancer in a multidimensional way. Implications for Practice: Nurses play crucial roles in society, including enhancing cancer awareness, delivering counseling services, and providing health education. Cancer nursing should plan cancer awareness training in line with individuals' attitudes toward cancer and encourage positive attitudes that increase participation in screening programs. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Volume

40

Issue

6

URI

https://doi.org/10.1016/j.soncn.2024.151728
https://hdl.handle.net/20.500.12450/6030

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  • PubMed İndeksli Yayınlar Koleksiyonu [458]
  • Scopus İndeksli Yayınlar Koleksiyonu [1574]
  • WoS İndeksli Yayınlar Koleksiyonu [2182]



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