Benefit finding predicts depressive and anxious symptoms in women with breast cancer
- PMID: 26003313
- DOI: 10.1007/s11136-015-1001-z
Benefit finding predicts depressive and anxious symptoms in women with breast cancer
Abstract
Purpose: The aims of this study were to examine the psychometric properties of Chinese version of the Benefit Finding Scale (BFS-C) and to evaluate the effect of benefit finding on depressive and anxious symptoms in Chinese women with breast cancer.
Methods: The English version of the Benefit Finding Scale was translated and back-translated prior to its administration. At the baseline assessment (T1), 658 women with breast cancer completed a demographic form, BFS-C, and Hospital Anxiety and Depression Scale (HAD). Then, 4 weeks later (T2), all the participants finished HAD again, and the BFS-C was re-administered to 100 patients who were randomly selected from the total sample.
Results: The BFS-C exhibited moderate internal consistency and test-retest reliability. Five factors were extracted by principal component analysis: personal growth, worldview, family relationship, social relationship, and acceptance, and confirmatory factor analyses supported this five-factor model. Regression analyses showed that more benefit finding at T1 could predict less depressive and anxious symptoms at T2, accounting for 21.1 % and 15.3 % of variance, respectively. More worldview, family relationship, social relationship, and acceptance at T1 were associated with less depressive symptoms at T2, and more personal growth, worldview, family relationship, and acceptance at T1 were associated with less anxious symptoms at T2.
Conclusions: BFS-C is of good reliability and validity, and appropriate for assessing benefit finding in women with breast cancer. Worldview, family relationship, and acceptance are important contributing factors to depressive and anxious symptoms, which may be beneficial for women with breast cancer.
Keywords: Anxious symptom; Benefit finding; Breast cancer; Chinese women; Depressive symptom.
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