医学
财务困境
苦恼
毒性
癌症
免疫疗法
心理困扰
重症监护医学
精神科
内科学
临床心理学
焦虑
金融体系
经济
作者
Yun‐Hsiang Lee,Chia-Li Siao,Hui Ying Yang,Yeur‐Hur Lai,Y. Liang,Yu-Fan Chen,Mei-Chih Wu
标识
DOI:10.1016/j.ejon.2023.102486
摘要
Abstract
Purpose
To examine the unmet care needs (i.e., overall needs and need subdomains [physical and daily living needs, psychological and emotional needs, care and support needs, and health-system and informational needs]) of patients with cancer undergoing immunotherapy alone or in combination with other anticancer therapies, as well as related influencing factors. Methods
A cross-sectional design was adopted. Cancer patients who received immunotherapy completed consent and questionnaires. Unmet care needs were evaluated with the Chinese version of the Supportive Care Needs Survey Screening Tool, symptom severity with the Symptom Severity Scale, distress severity with the Distress Thermometer Scale, and financial toxicity using the Financial Toxicity - Functional Assessment of Chronic Illness Therapy Questionnaire. Results
In total, 105 patients were surveyed. The most frequently reported unmet needs were psychological and emotional needs (56.2%) followed by health-system and informational needs (36.2%). The major factors associated with unmet care needs and their subdomains were years of education, symptoms, distress, and financial toxicity. Years of education predicted overall unmet care needs, psychological and emotional needs, and care and support needs; symptoms predicted overall unmet care needs and all four subdomains; distress predicted psychological and emotional needs and health-system and informational needs; and financial toxicity predicted overall needs and psychological and emotional needs. Conclusions
Patients with higher education, severe symptoms, distress, and financial toxicity reported more unmet care needs. The findings of this study could be incorporated into immunotherapy-related clinical practice guidelines and future interventions to improve the quality of cancer care.
科研通智能强力驱动
Strongly Powered by AbleSci AI