Suppr超能文献

PROMIS 自我效能感衡量慢性疾病管理的多维性。

Multidimensionality of the PROMIS self-efficacy measure for managing chronic conditions.

机构信息

Department of Occupational Therapy, University of Florida, PO BOX 100164, Gainesville, FL, 32610, USA.

Center of Innovation on Disability and Rehabilitation Research (CINDRR), Department of Veterans Affairs, 101 SE 2nd Place Ste 104, Gainesville, FL, 32601, USA.

出版信息

Qual Life Res. 2019 Jun;28(6):1595-1603. doi: 10.1007/s11136-019-02116-w. Epub 2019 Feb 26.

Abstract

PURPOSE

This study investigated the PROMIS Self-Efficacy Measure for Managing Chronic Conditions (PROMIS-SE) domain distributions and examined the factor structure of the PROMIS-SE.

METHODS

A total of 1087 individuals with chronic conditions participated in this study. PROMIS-SE's item banks and two short forms (eight-item and four-item) measuring five behavioral domains (daily activities(DA), Emotions(EM), medications and treatments(MT), social interactions(SS), and Symptoms(SX)) were examined. PROMIS-SE's T-score ranges and distributions were examined to identify domain metric distributions and confirmatory factor analysis (CFA) was conducted to test a multidimensional model fit to the PROMIS-SE.

RESULTS

PROMIS-SE domains showed different T-score ranges and distributions for item banks and two short forms across all five domains. While PROMIS-SE EM demonstrated the highest T-scores (least negatively skewed), MT had the lowest T-scores (most negatively skewed) for all three forms. In general, respondents were more likely to achieve highest self-efficacy ratings (very confident) for domains DA, MT, and SS as compared to domains EM and SX. CFA confirmed that a multidimensional model adequately fit all three PROMIS-SE forms.

CONCLUSION

Our results indicate that self-efficacy T-distributions are not consistent across domains (i.e., managing medications and treatments domain was more negatively skewed difficult than other domains), which is a requirement for making inter-domain comparisons. A multidimensional model could be used to enhance the PROMIS-SE's estimate accuracy and clinical utility.

摘要

目的

本研究调查了 PROMIS 管理慢性疾病自我效能度量表(PROMIS-SE)的各分量表分布,并检验了 PROMIS-SE 的因子结构。

方法

共 1087 名患有慢性疾病的个体参与了这项研究。研究考察了 PROMIS-SE 的项目库以及两个用于测量五个行为领域(日常活动(DA)、情绪(EM)、药物和治疗(MT)、社会互动(SS)和症状(SX))的简短形式(八项目和四项目)。考察了 PROMIS-SE 的 T 分数范围和分布,以确定各分量表的度量分布,并进行验证性因子分析(CFA),以检验 PROMIS-SE 的多维模型拟合情况。

结果

在所有五个领域中,PROMIS-SE 各分量表的项目库和两个简短形式都显示出不同的 T 分数范围和分布。虽然 PROMIS-SE 的 EM 表现出最高的 T 分数(最小负偏斜),但 MT 在所有三种形式中的 T 分数最低(最负偏斜)。总体而言,与 EM 和 SX 相比,受访者在 DA、MT 和 SS 等领域更有可能获得最高的自我效能评分(非常有信心)。CFA 证实多维模型适用于所有三种 PROMIS-SE 形式。

结论

我们的结果表明,自我效能 T 分布在各分量表之间并不一致(即,管理药物和治疗的领域比其他领域更负偏斜困难),这是进行跨分量表比较的要求。多维模型可用于提高 PROMIS-SE 的估计准确性和临床实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f8c/6525059/296a91cc1e9d/nihms-1522620-f0001.jpg

相似文献

引用本文的文献

本文引用的文献

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验