Psychometric evaluation of the Chinese breast cancer prevention trial symptom scale

Background

Women with breast cancer experience a wide spectrum of symptoms after diagnosis and treatment. Symptoms experienced by this specific population might not be fully assessed using available traditional Chinese-language symptom measures.

Objectives

The aim of this study was to examine the latent constructs and psychometric properties of the Chinese Breast Cancer Prevention Trial (C-BCPT) Symptom Scale.

Methods

Two hundred women with breast cancer were recruited in Taiwan. Psychometric properties, including construct validity, internal consistency, and test-retest reliability, of the C-BCPT Symptom Scale were tested after translating the original instrument.

Results

A 21-item C-BCPT Symptom Scale, with 7 extracted factors accounting for 72.26% of the total variance, resulted from an exploratory factor analysis. Construct validity was confirmed by significant correlations between scores on the C-BCPT Symptom Scale and the Taiwan-version Short Form-36 Health Survey (r = -0.49 to -0.53)/Greene Climacteric Scale (r = 0.81). Reliability coefficients for the overall scale/6 extracted factors (Cronbach’s ? = 0.72-0.88) and test-retest reliability (intraclass correlation coefficients = 0.77-0.94) of the translated instrument were satisfactory, whereas 1 reliability coefficient for 1 extracted factor was inadequate (Cronbach’s ? = 0.57).

Conclusion

An interpretable structure with preliminary acceptable psychometric properties of the C-BCPT Symptom Scale was obtained; the C-BCPT can help traditional Chinese-speaking healthcare professionals perform adequate assessments of the symptoms experienced by women with breast cancer.

Implications for practice

The C-BCPT Symptom Scale can be used in clinical practice and research to assess symptoms experienced by this specific population or effects of related interventions.

Authors C F Tsai, P S Tsai, C L Liu, Y K Lin, Y L Ling, I J Tseng, Y M Liao

Journal Cancer nursing

Therapeutic Area Oncology

Center of Excellence Health Economic Modeling & Meta-analysis

Year 2017

Region China

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