A Scoping Review of Patient Navigation in the Continuity of Cancer Care for Women

女性癌症患者连续性护理中患者导航的范围界定综述

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Abstract

BACKGROUND: Cancer is a significant cause of morbidity and mortality among women worldwide, particularly breast and cervical cancers. Continuity of care (CoC) is essential for effective cancer management but is often hindered by systemic, social, and economic barriers. Patient navigation has emerged as a promising intervention to improve access and coordination across the cancer care trajectory. PURPOSE: This review aimed to explore how patient navigation has been applied to support continuity of cancer care for women. METHODS: This scoping review was conducted based on the framework developed by Arksey and O'Malley (2005). Comprehensive literature search across five electronic databases, including CINAHL, PubMed, ScienceDirect, Scopus, Taylor & Francis, and search engine: Google Scholar. Eligible studies included English-language, original articles that involved women aged 18 years or older, focusing on the impact of patient navigation on continuity of care for cancer. Data were extracted independently by three reviewers using a standardized form. A thematic analysis approach was used to synthesize the findings across key domains. RESULTS: A total 26 studies were included, most of which were conducted in the United States. Patient navigation demonstrated positive impacts across the cancer care continuum: (1) improved screening, especially in minority populations; (2) reduced time to diagnostic resolution; (3) improved patient treatment and rehabilitation. Five thematic domains were identified: care coordination, education/information, empowerment, emotional and social support, and logistical or financial assistance. CONCLUSION: This scoping review provides an overview of how patient navigation contributes to strengthening the continuity of cancer care for women and identifies gaps in its current implementation across settings. Future research should investigate scalable and culturally adapted navigation models in low-resource settings, evaluating their cost-effectiveness and long-term outcomes.

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