Latent Profile Analysis of the Uncertainty Experienced among Parents of Children with Central Nervous System Tumors: A Cross-Sectional Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: The study aims to identify and characterize the latent categories of illness uncertainty among parents of children diagnosed with central nervous system tumors, examine the influencing factors, and propose targeted nursing strategies. Methods: Conducted as a cross-sectional study, this research utilized convenience sampling to recruit parents of children undergoing treatment for central nervous system tumors at a tertiary hospital in Guangzhou between October 2023 and July 2024. Data collection involved a general information questionnaire, the Illness Uncertainty Scale, the Self-Rating Anxiety Scale, and the Self-Rating Depression Scale. Latent profile analysis was employed to determine the potential categories of illness uncertainty, while binary logistic regression analysis was used to assess the influencing factors. Results: The study included 108 parents, whose illness uncertainty was categorized into two distinct groups: “Information Acceptance Group” (n = 66, 61.1%) and “Cognitive Deficiency Group” (n = 42, 38.9%). The binary logistic regression analysis identified the gender and anxiety level of the parents as significant influencing factors in the categorization of illness uncertainty. Conclusion: The study reveals distinct categorical characteristics of illness uncertainty among parents of children with central nervous system tumors. It is recommended that nursing professionals implement gender-specific psychological interventions, provide informational support, and employ positive reinforcement strategies to mitigate parental anxiety, reduce illness uncertainty, and enhance the overall quality of pediatric care.
Source: Journal of Cancer Therapy
Authors: Dan Zhang, Hengxue Wang, Lan Bai, Chunju Xiao, Daili Zou
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411387164