Development of a Readiness for Hospital Discharge assessment tool in Thai patients with stroke

针对泰国中风患者开发出院准备评估工具

阅读:1

Abstract

BACKGROUND: The transition from hospital to home among patients with stroke is quite challenging. If the patients are not ready for hospital discharge, their condition may worsen, which also causes a high rate of readmission. Although instruments to measure readiness for hospital discharge exist, none of them fit with the Thailand context. OBJECTIVE: This study aimed to develop a Readiness for Hospital Discharge assessment tool in Thai patients with stroke. METHODS: The study was conducted from February to September 2020, which consisted of several steps: 1) conducting an extensive literature review, 2) content validity with five experts, 3) pilot testing with 30 samples, and 4) field testing with 348 participants. Content validity index (CVI) was used to measure the content validity, Cronbach's alpha and inter-item correlation to evaluate reliability, and multiple logistic regression analysis to measure the construct validity. RESULTS: The findings showed good validity and reliability, with I-CVI of 0.85, Cronbach's alpha of 0.94, and corrected item-total correlation ranging from 0.43 to 0.86. The construct validity was demonstrated through the results of regression analysis showing that the nine variables include level of consciousness (OR = 0.544; CI 95% = 0.311 - 0.951), verbal response (OR = 0.445; 95% CI 0.272- 0.729), motor power right leg (OR = 0.165; 95% CI 0.56- 0.485), visual field (OR = 0.188; 95% CI 0.60-0.587), dysphagia (OR = 0.618; 95% CI 0.410-0.932), mobility (OR = 0.376; 95% CI 0.190 - 0.741), self-feeding (OR = 0.098; 95% CI 0.036 -0.265), bathing (OR = 0.099; 95% CI 0.026-0.378), and bladder control (OR = 0.589; 95% CI 0.355-0.977) that significantly influenced the hospital readmission within 30 days in patients with stroke. CONCLUSION: The Readiness for Hospital Discharge assessment tool is valid and reliable. Healthcare providers, especially nurses, can use this tool to assess discharge conditions for patients with stroke with greater accuracy in predicting hospital readmission.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。