Effectiveness of integrated care models for stroke patients: A systematic review and meta-analysis.

被引量：- 发表：1970

Decoding machine learning in nursing research: A scoping review of effective algorithms.

被引量：- 发表：1970

The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.

Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field. Systematic review of prospective studies. CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol. Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes. Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health. We have registered our review in PROSPERO (registration number: CRD 42023415232).

被引量：- 发表：1970

Are we making the most of safe staffing research.

The uptake of research evidence on staffing issues in nursing by nursing leadership, management and into organizational policies seems to vary across Europe. This study wants to assess this uptake of research evidence. Scoping survey. The presidents of twelve country specific Sigma Chapters within the European Region answered written survey questions about work organisation, national staffing levels, national skill mix levels, staff characteristics, and education. Seven of the 12 chapters could not return complete data, reported that data was unavailable, there was no national policy or only guidance related to some settings. Enhancing the awareness of nursing research and of nursing leaders and managers regarding staffing level evidence is not enough. It seems necessary to encourage nurse leaders to lobby for staffing policies. Research evidence on staffing issues in nursing and how it benefits health care is available. In Europe this evidence should be used more to lobby for change in staffing policies.

被引量：- 发表：1970

Nurses during war: Profiles-based risk and protective factors.

Nurses in southern Israel's public hospitals were exposed to unusual traumatic events following the October 7, 2023, Hamas attack on Israel, and the ensuing Swords of Iron War. This study aimed to clarify the complexity of wartime nursing by identifying profiles based on risk factors (i.e., psychological distress and adjustment disorders) and protective factors (i.e., positive affect (PA), resilience, and perceived social support [PSS]). This study utilizes a cross-sectional design. Two hundred nurses at a major public hospital in southern Israel completed self-report questionnaires. A latent profile analysis (LPA) was conducted to identify distinct profiles based on nurses' risk and protective factors. Differences in profiles were examined alongside sociodemographic and occupational variables and traumatic event exposure. The LPA was conducted using MPlus 8.8 Structural Equation Modeling (SEM) software. Two distinct profiles were identified: "reactive" and "resilient." The "reactive" group included nurses who had higher risk factor scores (psychological distress and adjustment disorder), whereas the "resilient" group included nurses who had higher protective factor scores (PA, resilience, and PSS). Furthermore, nurses in the "reactive" group were younger, with greater seniority, worse self-rated health, and a higher frequency of kidnapped family members compared to nurses from the "resilient" group. Nurses in wartime are at risk if identified as "reactive." Identifying these profiles can assist in developing effective support practices to help nurses cope with wartime challenges and maintain their mental well-being. Healthcare organizations should tailor interventions to prepare and support nurses of various ages and experience levels, during and after conflicts. This approach aims to reduce risk factors and promote protective factors among nurses during wartime.

被引量：- 发表：1970