Introducing a named nurse model of care in a community nursing service.
Published In: Primary Health Care, 2024, v. 34, n. 6. P. 35 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Jones, Donna 3 of 3
Abstract
Why you should read this article: • To recognise the challenges that community nurses commonly experience in practice • To learn about the benefits of implementing a named nurse model of care in community settings • To be aware of the challenges with introducing changes in practice such as the named nurse model. The named nurse model has the potential to promote person-centred, high-quality care in the community setting, while also enhancing the job satisfaction, morale and retention of community nurses. By giving responsibility for a small group of patients to a named community nurse, any deterioration in their health can be identified quickly, resulting in a reduction in patient safety incidents. Additionally, the continuity of the named nurse model can foster therapeutic relationships, enhancing experiences of care for both patients and nurses. This article details a service evaluation project in which the named nurse model was introduced across a trust-wide community nursing service. Following the introduction of the model, the capacity of the service increased, and the quality of care provided by nurses improved. However, it was identified that some community nurses experienced moral distress when reprioritising patient care to maintain service capacity. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Primary Health Care. 2024/12, Vol. 34, Issue 6, p35
- Document Type:Article
- Subject Area:Nursing and Allied Health
- Publication Date:2024
- ISSN:0264-5033
- DOI:10.7748/phc.2024.e1824
- Accession Number:181249823
- Copyright Statement:Copyright of Primary Health Care is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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