Artículo

Informing nursing policy: An exploration of digital health research by nurses in England

Resumen

Aims Digital health technologies are designed, implemented, and evaluated to support clinical practice, enable patients to self-manage illness, and further public and global health. Nursing and health policies often emphasise the importance of evidence-based digital health services to deliver better care. However, the contribution nurses make to digital health research in many countries is unknown. Hence, this study aims to examine digital health research conducted by nurses in England. Design A bibliometric analysis. Methods The CINAHL, MEDLINE, and Scopus databases were searched between 2000 and 2022, and supplemented with a hand search of nurses’ research profiles. Results were screened by title, abstract, and full text against eligibility criteria. Data were extracted and bibliometric analysis used to summarise the findings. Results Mental health nurses produced the most digital health research in England, followed by nurses working in community care, with several disciplines underrepresented or missing. Web/online health services or information was the most researched technology, followed by mobile health and telehealth. Nurses based in the south-east and north-west of England produced the most digital health research, with other regions less well represented. Conclusion Nurse leaders should support nurses to conduct more digital health research by providing dedicated time, funding, and professional development opportunities, particularly in under researched clinical areas, technologies, and geographic regions to further evidence-based practice and patient care. More digital nursing data is needed to support nurse led research in areas like artificial intelligence and data science. The findings supported the national Philips Ives Review by identifying areas of digital nursing research that need more investment in England.
Autores
Vanegas-Ayala, SC; Barón-Velandia, J; Leal-Lara, DD
Título
A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems
Afiliaciones
Universidad Distrital Francisco Jose de Caldas
Año
2022
DOI
10.1155/2022/8483003
Tipo de acceso abierto
gold
Referencia
WOS:000771080400001
Artículo obtenido de:
WOS
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