The utility of structure-process-outcome informed psychosocial nursing for lung cancer patients with brain metastases and anxiety
Abstract
AIM: To investigate the application value of psychological support based on the Structural-Process-Outcome three-dimensional quality analysis framework for patients with lung cancer brain metastasis and anxiety. Methods: Using the random number table method, 55 patients with lung cancer brain metastasis admitted to Xing’an League People’s Hospital from January 2020 to December 2023 were randomly divided into an observation group (28 cases) and a control group (27 cases). The control group received routine continuous nursing intervention measures; the observation group, in addition to the control group’s measures, received psychological support under the framework of the Structural-Process-Outcome three-dimensional quality analysis. Both groups of patients were followed up for 20 weeks. The differences in psychological state scores, quality of life levels, and nursing satisfaction between the two groups before and after nursing intervention were compared. Results: After 20 weeks of nursing intervention, the GAD-7 and SPBS scores in the emotional, physical, and economic dimensions of the observation group were lower than those of the control group (P < 0.05), and the EORTC QLQ-C30 scores in the physical, role, emotional, cognitive, and social functional domains were higher than those of the control group (P < 0.05), while the fatigue, pain, and nausea and vomiting symptom domains were lower than those of the control group (P < 0.05). The nursing intervention satisfaction was 100.00%, higher than the control group’s 81.48% (P < 0.05). Conclusion: Psychological support, guided by the structural-process-outcome three-dimensional quality analysis framework, can effectively improve the psychological well-being and quality of life in patients with lung cancer brain metastasis and anxiety. Moreover, it significantly enhances nursing satisfaction, underscoring its clinical value
Copyright (c) 2026 Yu Xiang, Lingling Li, Zhuo Liu
This work is licensed under a Creative Commons Attribution 4.0 International License.
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