Barriers and facilitators to artificial intelligence adoption among nursing students: a mixed-methods study.

Palliative care needs in low- and middle-income countries (LMICs) are vast and increasing rapidly. An estimated 16.4 million people died with serious health-related suffering in LMICs which required palliative care in 2015. This number is projected to nearly double by 2060, representing 83% of global serious suffering due to aging and the shift to non-communicable diseases. However, access to palliative care in LMICs remains deeply inequitable compared to high-income countries (HICs). While available services in HIC cover up to 81% of the need, coverage in LMICs reaches only 10-37%. In response, various strategies have been proposed to expand access and improve outcomes, including home-based palliative care (HBPC). HBPC provides holistic support to patients with advanced disease and their families by addressing physical, psychological, and social needs in the homes. Evidence shows that HBPC increases the likelihood of dying at home, reduces symptoms, and improves quality of life for patients. Programs that integrate specialist and primary care achieve these outcomes while also reducing costs, particularly when they include standardised interdisciplinary meetings, volunteer engagement, and early intervention. While multiple reviews have demonstrated the effectiveness of HBPC, most originate from HICs. Only a recent integrative review of HBPC model from South-East-Asia found that current HBPC only focus on symptom management with limited family support. However, a major gap remains: no synthesis has examined which components of HBPC work, why they work, and in which context they can be implemented successfully in LMIC countries. This gap limits our understanding of how HBPC models can be adapted and transferred to low-resource settings, where constrained resources require careful selection and modification of interventions. Addressing this gap would help ensure that LMICs can learn from existing models without relying on one-size-fits-all approaches developed in HICs.