Development and psychometric validation of a culturally adapted instrument to assess nurses' competence in delivering bad news to families of patients nearing end-of-life.

Maternal health outcomes in East Africa remain a pressing concern, with high mortality rates persisting in the region [, ]. In rural and peri-urban areas, mothers face numerous barriers to care, among which language and communication gaps are especially critical [–]. In linguistically diverse countries like Uganda, which has over 40 languages [], clinical encounters frequently occur in nonnative languages. This linguistic mismatch compromises diagnostic accuracy, shared decision-making, and patient trust [–], ultimately elevating the risk of adverse outcomes and delayed emergency care [, ]. While existing mHealth platforms have improved healthcare access [–], they often lack interactivity and exclude non-literate populations []. Large Language Models (LLMs) offer promising solutions for accessible, conversational patient education. However, their effectiveness in low-resource environments is fundamentally constrained by a lack of high-quality, domain-specific training datasets in local languages []. Existing models remain overwhelmingly English-centric. Although multilingual African NLP initiatives are growing [–], there remains a severe scarcity of clinically grounded, in-language corpora specifically focused on maternal health []. To address this critical gap, we introduce the dataset: a multilingual, clinically validated question-and-answer corpus in English, Luganda, Runyankore, and Swahili. The primary objective of this dataset is to support the fine-tuning of LLMs for prenatal and postnatal care in East African languages. By providing a culturally tailored and medically accurate foundation, this work aims to enable the development of conversational AI tools that are linguistically appropriate. This resource bridges critical data gaps in low-resource settings, contributing to an equitable digital health ecosystem that empowers mothers and frontline healthcare workers with accessible information.