Quality of Nursing Care and Adaptation to Artificial Intelligence in Low and Middle Income Countries: A Systematic Review of Empirical Studies

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Authors: Fernan Torreno, FRINCESS FLORES
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Abstract

This dataset supports a systematic review on quality of nursing care and nurses’ adaptation to artificial intelligence (AI) in low‑ and middle‑income countries (LMICs). It compiles structured study‑level data from ten empirical and review studies (2022–2025), covering missed nursing care, nurse staffing, work environment, nursesensitive outcomes, and AI‑related attitudes, literacy, and intention to use. Variables include bibliographic, contextual, design, and outcome information, organized into two domains: nursing care quality and AI adaptation. Risk‑of‑bias assessments (AMSTAR 2, JBI) are summarized, and data are provided in machine‑readable formats with consistent identifiers for linkage.

Fields of Study & Subjects

Nursing education and management Global Health Workforce Issues Healthcare Education and Workforce Issues

Publication details

Journal / source
Mendeley Data
Publication date
27 January 2026
Document type
Dataset