Atreya, S. and Rao, A. and Dhyani, V.S. and Mathew, M. and Gursahani, R. and Simha, S. and Preston, N. and Walshe, C. and Salins, N. (2024) Exploring the contribution of cancer palliative care development toward alleviating the human crisis of suffering in low- and middle-income countries : A framework synthesis protocol. Palliative and Supportive Care. ISSN 1478-9515
Full text not available from this repository.Abstract
Objectives Inadequate access to cancer care, high mortality, and out-of-pocket expenditure contribute to health-related suffering in low- and middle-income countries, making palliative care a relevant option. How palliative care development has alleviated suffering is not systematically studied, necessitating this review’s conduct. The objective of this systematic review with a framework synthesis approach is to identify and map the dimensions and indicators of cancer palliative care development and the components of integration between cancer and palliative care in LMICs. Methods Uni- and multi-disciplinary databases like Cochrane, MEDLINE (PubMed), EMBASE, CINAHL Complete, and PsycINFO will be systematically searched for eligible studies exploring cancer palliative care development in LMICs and their contribution to alleviating health-related suffering in the cancer context. Our selection process will encompass countries classified by the World Bank as low-income (26 countries), lower-income (54 countries), and upper-middle-income (54 countries). Results Review findings will be synthesised and analysed using a best-fit framework synthesis method using 2 frameworks (the WHO model of components and indicators for palliative care development and integration elements between oncology and palliative care), and the findings will be developed as themes and subthemes, and patterns interpreted using these 2 models. Significance of results This review will analyse the development of cancer palliative care in LMICs. It will identify gaps in provision, solutions derived at the regional level to address them, and best practices and failed models with reasons underpinning them.