Content validation of the nursing diagnosis "ineffective protection" in the context of adolescents with cancer

被引:0
作者
Nunes, Marilia Mendes [1 ]
Lopes, Marcos Venicios de Oliveira [1 ]
Silva, Viviane Martins da [1 ]
Pascoal, Livia Maia [2 ]
Cavalcante, Yanka Alcantara [1 ]
Moreira, Janaina Calisto [1 ]
机构
[1] Univ Fed Ceara, 1115 Alexandre Barauna, BR-60430160 Fortaleza, Brazil
[2] Univ Fed Maranhao, Ave Univ, BR-65915060 Imperatriz, MA, Brazil
来源
JOURNAL OF PEDIATRIC NURSING-NURSING CARE OF CHILDREN & FAMILIES | 2024年 / 79卷
关键词
Validation studies; Adolescence; Cancer; Nursing diagnosis; CONTENT VALIDITY;
D O I
10.1016/j.pedn.2024.09.003
中图分类号
R47 [护理学];
学科分类号
1011 ;
摘要
Objective: To analyze the relevance of a set of clinical elements to represent the content domain of the nursing diagnosis Ineffective Protection. Design and methods: This study employed content validation, using expert judgment to assess the appropriateness of clinical indicators, etiological factors, and conceptual and operational definitions, integrating the collective wisdom framework, the theory of predictive diversity, and the determination of rater skill level. Results: From the middle range theory, 28 clinical indicators and 17 etiological factors were identified, which were rated by 34 raters. The analysis showed that the clinical indicators restlessness and insomnia and the etiological factors ambient temperature and humidity were not considered relevant for the nursing diagnosis of Ineffective Protection, because the median content validity index was lower than 0.9. Conclusions: Content validation verified that most of the components of the nursing diagnosis of Ineffective Protection were considered relevant by judges. Practical implications: It is believed that the data obtained can help nurses to more easily evaluate the related factors and clinical indicators of Ineffective Protection presented by adolescents with cancer, which favors the process of diagnostic inference. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:164 / 170
页数:7
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