Yoshioka-Maeda Kyoko, Matsumoto Hiroshige, Honda Chikako, Taira Kazuya, Hosoya Noriko, Sato Miki, Iwasaki-Motegi Riho, Sumikawa Yuka, Fujii Hitoshi, Miura Takahiro, Shiomi Misa
Department of Community Health Nursing/Public Health Nursing, Division of Health Sciences & Nursing, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Public Health Nurs. 2025 May-Jun;42(3):1216-1225. doi: 10.1111/phn.13545. Epub 2025 Mar 13.
This study aimed to develop essential individual care needs assessment (EICNA) items and evaluate the validity of that judgment.
We used a sequential two-phase design for this study.
Item selection was conducted using φ coefficients between these items' values and the care need levels and discussions with supervisory PHNs. Phase 1 was a cross-sectional, nationwide survey of 275 mid-level public health nurses (PHNs) from 196 municipalities in Japan (December 2022 to January 2023), including 46 potential EICNA items. In Phase 2, PHNs piloted the EICNA items in clinical settings, entering data into a web-based system that used an algorithm to determine care need levels based on the weighted sum of 21 items (August 2023 to January 2024). Thereafter, the PHNs evaluated the appropriateness of the algorithm's judgments.
Twenty-one essential items were identified. Among 1867 cases, care need levels were categorized as low (n = 1008, 54.0%), moderate (n = 652, 34.9%), and high (n = 207, 11.1%), with 94.9% of PHNs considered the algorithm's classifications appropriate.
Twenty-one EICNA items were identified to assess the care needs, and the level of care needs determined by the weighted sum of these items was deemed appropriate by PHNs.
UMIN000051509 (https://www.umin.ac.jp/ctr/; August 1, 2023).
本研究旨在制定基本个人护理需求评估(EICNA)项目并评估该判断的有效性。
本研究采用了两阶段的连续设计。
使用这些项目的值与护理需求水平之间的φ系数以及与监督公共卫生护士(PHN)的讨论来进行项目选择。第一阶段是对来自日本196个城市的275名中级公共卫生护士(2022年12月至2023年1月)进行的全国性横断面调查,包括46个潜在的EICNA项目。在第二阶段,公共卫生护士在临床环境中试用EICNA项目,将数据输入基于网络的系统,该系统使用算法根据21个项目的加权总和来确定护理需求水平(2023年8月至2024年1月)。此后,公共卫生护士评估了算法判断的适当性。
确定了21个基本项目。在1867例病例中,护理需求水平分为低(n = 1008,54.0%)、中(n = 652,34.9%)和高(n = 207,11.1%),94.9%的公共卫生护士认为算法分类合适。
确定了21个EICNA项目来评估护理需求,公共卫生护士认为由这些项目的加权总和确定的护理需求水平是合适的。
UMIN000051509(https://www.umin.ac.jp/ctr/;2023年8月1日)。