Patient Satisfaction with Navigator Interpersonal Relationship (PSN-I): item-level psychometrics using IRT analysis.
Patient Satisfaction with Navigator Interpersonal Relationship (PSN-I): item-level psychometrics using IRT analysis.
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DOI：
10.1007/s00520-019-04833-x
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发表时间：
2020-02
期刊：
影响因子：
--
通讯作者：
Fiscella K
中科院分区：
文献类型：
--
作者：
Jean-Pierre P;Shao C;Cheng Y;Wells KJ;Paskett E;Fiscella K
患者导航（PN）是消除癌症健康不公平的一种有前途的干预措施。患者导航器在导航过程中起着关键作用。患者对导航器的满意度在确定PN程序的有效性方面很重要。我们应用项目反应理论（IRT）分析来建立患者与导航员人际关系满意度（PSN-I）的项目级心理测量属性。我们进行了验证性因素分析（CFA），以建立单维度的9项PSN-I在751例癌症患者（68%女性）之间的18和86岁。我们拟合一维IRT模型-无约束分级反应模型（GRM）和Rasch模型-PSN-I数据，并使用似然比（LR）检验和信息准则比较模型拟合。我们获得了项目参数估计（IPE），项目类别/操作特征曲线，项目/测试信息曲线更好的拟合模型。采用对角加权最小二乘法的验证性因素分析证实单因素模型拟合数据（RMSEA = 0.047，95%CI = 0.033-0.060，CFI = 1）。对PSN-I项目的答复分为第4和第5类。我们汇总了前三个响应类别，为两个IRT模型提供稳定的参数估计。GRM对数据的拟合显著优于Rasch模型（LR = 80.659，df = 8，p < 0.001）。赤池信息系数（6384.978 vs. 6320.319）和贝叶斯信息系数（6471.851 vs. 6443.771）较低的GRM。IPE对GRM的项目区分力有显著差异（1.80-3.35）。这IRT分析证实了潜在的结构PSN-I和支持其使用作为一个有效的和可靠的措施，潜在的满意度PN。
Patient navigation (PN) is a promising intervention to eliminate cancer health inequities. Patient navigators play a critical role in the navigation process. Patients’ satisfaction with navigators is important in determining the effectiveness of PN programs. We applied item response theory (IRT) analysis to establish item-level psychometric properties for the Patient Satisfaction with Interpersonal Relationship with Navigators (PSN-I). We conducted a confirmatory factor analysis (CFA) to establish unidimensionality of the 9-item PSN-I in 751 cancer patients (68% female) between 18 and 86 years old. We fitted unidimensional IRT models—unconstrained graded response model (GRM) and Rasch model—to PSN-I data, and compared model fit using likelihood ratio (LR) test and information criteria. We obtained item parameter estimates (IPEs), item category/operating characteristic curves, and item/test information curves for the better fitting model. CFA with diagonally weighted least squares confirmed that the one-factor model fit the data (RMSEA = 0.047, 95% CI = 0.033–0.060, and CFI ≈ 1). Responses to PSN-I items clustered into the 4th and 5th categories. We aggregated the first three response categories to provide stable parameter estimates for both IRT models. The GRM fit the data significantly better than the Rasch model (LR = 80.659, df = 8, p < 0.001). Akaike’s information coefficient (6384.978 vs. 6320.319) and Bayesian information coefficient (6471.851 vs. 6443.771) were lower for the GRM. IPEs showed substantial variation in items’ discriminating power (1.80–3.35) for GRM. This IRT analysis confirms the latent structure of the PSN-I and supports its use as a valid and reliable measure of latent satisfaction with PN.
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