Illness perception profile among cancer patients and its influencing factors: A cross-sectional study

社会心理的 医学 心理干预 疾病 感知 横断面研究 临床心理学 癌症 精神科 内科学 心理学 病理 神经科学
作者
Hualong Ma,Ke Hu,Weixin Wu,Qinyang Wu,Qiuyun Ye,Xiaohao Jiang,Lu Tang,Yongyue He,Qiaohong Yang
出处
期刊:European Journal of Oncology Nursing [Elsevier]
卷期号:69: 102526-102526 被引量:2
标识
DOI:10.1016/j.ejon.2024.102526
摘要

Abstract

Purpose

The purpose of this study was to explore latent profiles of illness perception among cancer patients and its influencing factors.

Methods

This study was a cross-sectional study adopting convenience sampling to select cancer patients from two hospitals in China. A total of 286 patients completed Brief Illness Perception Questionnaire, Post-traumatic Growth Inventory, Fear of Disease Progression Questionnaire and Psychosocial Adjustment to Illness Scale. Latent profile analysis and multiple linear regression were performed to explore the subgroups and factors influencing classification.

Results

Three subgroups were identified, which were labelled as "Moderate Illness Perception Group" (16.8%; C1), "High Illness Perception with Heightened Concerns Group" (68.5%; C2) and "High Resilience and Low Symptomatic Impact Group" (14.7%; C3). Specifically, "Normal", "Mild symptom" and "Bed time during the day <50%" of "Functional Status" were more associated with C3. "Worker", "Farmer" and "Self-employed" were more associated with C1 and C2. Patients who had more "knowledge of the disease" were more associated with C2 and C3, who had less "post-traumatic growth" were more associated with C1, and who had less "fear of disease progression" and more "psychosocial adjustment" were more associated with C3 (all P < 0.05).

Conclusions

There was significant variability of illness perception among three subgroups of cancer patients, which emphasized the complexity of psychological condition. The insights derived from these distinct profiles enables tailored interventions and patient-centered communication strategies. However, integrating objective measures or biomarkers is needed to complement self-reported data.
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