AI Insight
This study of 413 gynecologic and breast cancer patients identified three distinct patterns of illness uncertainty: low uncertainty with psychological adaptation (8.0%), moderate uncertainty with complexity distress (37.6%), and high uncertainty with cognitive ambiguity (54.4%). The research found that illness uncertainty partially mediated the relationship between social support and depressive symptoms, accounting for 22.3% of the total effect, and that factors including education level, caregiver type, and time since diagnosis were significantly associated with uncertainty patterns.
Why it matters
The identification of distinct uncertainty profiles suggests that psychosocial interventions for cancer patients could be more effective if tailored to individual uncertainty patterns rather than using one-size-fits-all approaches. Understanding that social support reduces depression partly through decreasing illness uncertainty provides specific targets for clinical intervention in cancer care settings.
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by Hui Zeng, Yan Lu, Quanping Zhao, Jingjing Gong, Li Mao, Yuxuan Wei, Xiaodan Li
Background
Gynecologic and breast cancers pose a significant global health burden for women. Illness uncertainty is a common psychological challenge among patients with gynecologic and breast cancers, particularly in settings with limited supportive care resources. This study aimed to identify latent classes of illness uncertainty, examine their associated factors, and investigate its mediating role between social support and depressive symptoms.
Methods
A cross-sectional study was conducted from December 2024 to June 2025, enrolling 413 patients from a tertiary hospital in Beijing using convenience sampling. Data were collected with a general information questionnaire, the Mishel Uncertainty in Illness Scale (MUIS), the Social Support Rating Scale (SSRS), the 9-item Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder-7 scale (GAD-7). Latent profile analysis was applied to identify subgroups of illness uncertainty. Univariate analysis and multinomial logistic regression were used to examine influencing factors. Structural equation modeling was employed to test the mediating effect.
Results
Three latent classes were identified: low uncertainty-psychological adaptation (8.0%), moderate uncertainty-complexity distress (37.6%), and high uncertainty-cognitive ambiguity (54.4%). Educational level, caregiver type, time since diagnosis, social support, and depressive symptoms were significantly associated with class membership. Mediation analysis revealed that illness uncertainty partially mediated the relationship between social support and depressive symptoms, with a significant indirect effect of −0.033 (95% CI: −0.056 to −0.016), accounting for 22.3% of the total effect.
Conclusions
This study revealed significant heterogeneity in illness uncertainty among patients with gynecologic and breast cancers, with class membership associated with multiple factors including caregiver type and time since diagnosis. Illness uncertainty appeared to mediate the relationship between social support and depressive symptoms. These findings may inform the development of stratified psychosocial interventions tailored to distinct uncertainty profiles, though further longitudinal research is needed to establish causal relationships.