Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74502
Title: Validation of the modified Chinese Cancer Survivor's Unmet Needs (CaSUN-C) for women with breast cancer
Authors: Fang, SY
Cheng, HR
Lin, CY 
Keywords: Breast cancer survivors
Chinese
Supportive care needs
Unmet needs
Validation
Issue Date: 2018
Publisher: John Wiley & Sons
Source: Psycho-oncology, 2018, v. 27, no. 1, p. 236-242 How to cite?
Journal: Psycho-oncology 
Abstract: Objective: The aims of the study were to modify the Cancer Survivor's Unmet Need (CaSUN) Scale into a short form and then test its psychometric scale-specific properties for breast cancer survivors in Taiwan. Methods: Using convenience sampling, recruited breast cancer survivors were separated into 2 samples (sample 1, n = 150, and sample 2, n = 162). First, we translated and modified the CaSUN to ensure cultural adaptation. Second, we used statistical methods to eliminate some items and conducted an exploratory factor analysis (EFA) using sample 1 to explore the factor structure of the CaSUN-C. Finally, we conducted a confirmatory factor analysis using sample 2 to confirm the structure suggested by the EFA and tested the criterion validity and known-group validity of the CaSUN-C. Results: Twenty items within 4 factors (information, physical /psychological, medical care, and communication needs) were identified for the CaSUN-C. Each factor had acceptable internal consistency (Cronbach's Alpha = .61 to .82). The criterion validity was supported by the significant correlations between the CaSUN-C scores and scores on fear of recurrence and depression. Known-group comparisons revealed that women who survived more than 60 months had fewer physical/psychological needs than those less 60 months, which supported the validity of CaSUN-C. Conclusion: The CaSUN-C demonstrated acceptable reliability and validity for assessing unmet needs among breast cancer survivors in Taiwan. Using this simple assessment to target the individual needs of these survivors can help healthcare professionals provide personalized care efficiently.
URI: http://hdl.handle.net/10397/74502
ISSN: 1057-9249
DOI: 10.1002/pon.4499
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