Duan Jiaobo, Fu Jufang, Gao Hongjie, Chen Changsheng, Fu Jianfang, Shi Xin, Liu Xufeng
School of Psychology, Fourth Military Medical University, Xi'an Shaanxi, China.
Infection Control Center of Xijing Hospital, Fourth Military Medical University, Xi'an Shaanxi, China.
PLoS One. 2015 Feb 9;10(2):e0116438. doi: 10.1371/journal.pone.0116438. eCollection 2015.
The English version of the Caregiver Quality of Life Index-Cancer (CQOLC) was translated into simplified Chinese (CQOLC-C), following cultural translation, back-translation and pretest steps. Three hundred and sixty one cancer caregivers participated in this study. Cronbach's alpha was used to assess CQOLC-C reliability. Exploratory factor analyses (EFA) was used to generate two models of the measure's factor structure, and confirmatory factor analyses (CFA) were used to test each model, such that the best model to explain the latent structure of the CQOLC-C was identified. EFA using different factor extraction methods yielded two models including four and eight factors. According to the CFA results, model 2 was better fit for the original study data, based on the RMSEA criterion [0.058(90% CI = 0.051-0.065)], χ2 (531) = 853.92, p < 0.0001; CFI (0.96), NNFI (0.96), IFI (0.97), and NFI (0.92). We also examined the effect of removing three items on the CQOLC-C factor structure and discuss the resulting differences from other versions. These results indicate that the CQOLC-C's factor structure does not fully fit the original theorized model. This study provides preliminary support for further use of the CQOLC-C. However, the present work provides only partial support for the relevance and construct validity of the scale for Chinese caregivers.
《癌症患者照顾者生活质量指数》（CQOLC）的英文版本在经过文化翻译、回译和预测试步骤后，被翻译成了简体中文（CQOLC-C）。361名癌症患者照顾者参与了本研究。使用克朗巴哈系数（Cronbach's alpha）来评估CQOLC-C的信度。探索性因素分析（EFA）用于生成该量表因素结构的两种模型，验证性因素分析（CFA）则用于检验每种模型，从而确定能够解释CQOLC-C潜在结构的最佳模型。采用不同因素提取方法的探索性因素分析产生了包含四个和八个因素的两种模型。根据验证性因素分析结果，基于RMSEA标准[0.058（90%置信区间=0.051-0.065）]、χ2（531）=853.92，p<0.0001；CFI（0.96）、NNFI（0.96）、IFI（0.97）和NFI（0.92），模型2更适合原始研究数据。我们还研究了删除三个项目对CQOLC-C因素结构的影响，并讨论了由此产生的与其他版本的差异。这些结果表明，CQOLC-C的因素结构并未完全符合原始的理论模型。本研究为CQOLC-C的进一步应用提供了初步支持。然而，目前的工作仅部分支持该量表对中国照顾者的相关性和结构效度。
