Contemporary nurse最新文献

Background: Studies of group versus individual education for T2DM have shown favourable outcomes but evidence to support group-based education for patients affected by obesity and T2DM is scant.

Methods: A parallel-group, randomised pilot trial was designed to determine the feasibility and acceptability of group-based education compared to individualised (usual care) education for patients affected by obesity, and T2DM requiring insulin. Adults (>18 years, N = 51) completing the 'Living Healthy with Diabetes' education program in a specialist bariatric service, with a body mass index (BMI) of greater than 35 kg/m² and T2DM requiring more than 1u/kg/day of insulin were invited to participate. Program delivery was one-on-one (Control, n = 25) or in group sessions (Intervention, n = 26), face-to-face in clinic, or via telehealth during COVID-19 lockdowns. Feasibility endpoint measures were recruitment, protocol adherence, participation rates, and engagement. Secondary endpoints were depression and anxiety, quality of life, sleepiness, body weight, diabetes distress score, metabolic parameters, and daily insulin dose.

Results: Protocol adherence and participation rates indicate group sessions were feasible and acceptable. There were no group differences in anthropometric or biochemical baseline measures, 12-month follow-up was complete. Both groups reduced and sustained weight loss with significant within-group improvements in diabetes distress score and daily dose of Insulin. Improvements in health-related quality of life were evident within and between groups but there was no evidence to indicate a difference in the effectiveness of the two educational approaches on weight loss.

Conclusions: Group-based education is feasible and acceptable but efficacy in achieving superior outcomes compared to individual education needs further investigation. Optimising nurse-led education delivery methods could enhance intervention effectiveness for patients affected by obesity and T2DM.

Background: Postoperative delirium is believed to be preventable in up to 40% of all cases. Researchers have proposed various preoperative risk prediction models for postoperative delirium in patients undergoing cardiac surgery, however, no consensus exists on which model is the most suitable.

Aim: To identify and map existing preoperative risk prediction models, detecting cardiac surgery patients at elevated risk of developing postoperative delirium.

Design: This scoping review considered cohort and case-control studies eligible if they developed or validated preoperative prediction models for postoperative delirium, in adult patients admitted for cardiac surgery via sternotomy.

Data sources: The primary search was conducted on May 6th, 2022, and a secondary search was conducted on September 18th, 2024. We searched MEDLINE, CINAHL, Embase, and PsycINFO where 2126 references were identified and 15 were included for full-text analysis.

Method: This scoping review was conducted in line with the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (the PRISMA-ScR) guideline.

Results: Twelve unique risk prediction models and three validation studies were included in this review, comprising between 77 and 45,744 participants. In total, 157 candidate prognostic variables were investigated of which 40 had a predictive value and thus, were included in the prediction models. The included models revealed an AUC from 0.68-0.93 in the derivation cohorts and 0.61-0.89 in the validation cohorts.

Conclusions: Twelve unique prediction models and 3 validation studies were identified and mapped. Collectively, the models demonstrated an AUC ranging from 0.61-0.93, indicating a fair to good discrimination performance.

Protocol registration: A protocol is registered at Open Science Framework (OSF) https://osf.io/wr93y/?view_only=d129c3bb6be04357bac35c2c41ba2a40.

Purpose: Cardiac rehabilitation (CR) is an effective treatment to reduce the burden of cardiovascular disease (CVD) but is underutilized. This study characterized CR enrollment barriers and perceived physician endorsement of CR in patient subgroups at increased risk of poor outcomes.

Materials and methods: The association between sociodemographic and clinical characteristics and Cardiac Rehabilitation Barriers Scale (CRBS) item and subscale scores were examined using secondary data analysis of patients with acute coronary syndrome referred to, but not yet enrolled in, a 12-week CR program. Participants rated perceived strength of recommendation to attend CR on 1-5 scale.

Results: The three most endorsed CRBS items were inclement weather, travel, and work responsibilities. Additional barriers (e.g. time constraints, already exercising, family responsibilities) emerged in certain patient subgroups. Perceived strength of physician endorsement was high in the overall sample. After statistical adjustment for confounds, depressed mood was positively associated with logistical (b = 0.05, p = 0.002), and comorbidity-related barriers (b = 0.02, p < 0.001). Female sex (b = 0.62, p = 0.004), higher body mass index (b = 0.05, p = 0.009), and diabetes (b = 1.08, p < 0.001), were associated with logistical barriers.

Conclusions: Patients require individualized support to address CR enrollment barriers. Given their crucial role in supporting patients to access CR, nurses are well-positioned to identify and address CR barriers.

Background: Lacking nursing workforce is a persistent and predicted issue that negatively affects the nursing team's stability, clinical care quality and patient safety. Prior research has examined the significant effect of work-family conflict on turnover intention, but the exact mechanism remains unclear.

Objective: To examine how work-family conflict influenced nurses' turnover intention in China. To investigate the mediating effect of psychological capital in work-family conflict affecting turnover intention; and to investigate the moderating effect of transformational leadership and age on work-family conflict, psychological capital and turnover intention.

Design: A cross-sectional survey of 1235 nurses employed in 4 hospitals within a single province in China that provided care to approximately 5 million people each year.

Methods: Several measures including Work-Family Conflict Scale, Turnover Intention Scale, Psychological Capital Questionnaire and Transformational Leadership Questionnaire were utilized to collect data. Structural equation modeling approach as well as the multi-group analysis were conducted.

Results: Via psychological capital, work-family conflict significantly affected the nurses' turnover intention indirectly (β = 0.08, p < 0.001). The mediation association was significantly moderated by transformational leadership (p < 0.001).

Conclusion: The association of work-family conflict affecting turnover intention among Chinese nurses was mediated by psychological capital, and transformational leadership moderated the relationship of work-family conflict, psychological capital and turnover intention.

Background: Emergency room nurses often experience high levels of stress and emotional exhaustion due to the fast-paced and high-stakes nature of their work. Understanding the psychological traits that buffer against burnout and enhance job performance is essential for promoting resilience in this critical workforce.

Aim: This study examined the effects of grit and subjective risk intelligence on job performance and burnout among emergency nurses and explored whether social cooperation moderates these relationships.

Study design: A cross-sectional quantitative study was conducted with 172 emergency nurses from two public hospitals. Data were collected via self-administered questionnaires. Regression and moderation analyses using Hayes' Process model were applied to test the hypotheses.

Results: Grit (specifically consistency of interest) and a positive attitude toward uncertainty were positively associated with job performance and negatively associated with burnout. Social cooperation significantly moderated the relationship between consistency of interest and job performance. However, its moderating role was limited across other variables.

Conclusions: Grit and subjective risk intelligence play significant roles in enhancing performance and reducing burnout among emergency nurses. Social cooperation can further strengthen the impact of grit on work outcomes, though its overall moderating effect may be context-dependent.

Relevance to clinical practice: Fostering psychological traits such as grit and risk intelligence in emergency nurses can support sustainable performance and emotional resilience. Nursing managers should consider incorporating psychological resilience training and team-based support structures into workforce development strategies.

Impact statement: Psychological traits like grit and risk intelligence reduce burnout and improve performance among emergency nurses through social cooperation.

Objective: This study aims to investigate how barcode-assisted medication administration systems (BCMAs) can affect medication administration errors (MAEs) in non-unit-dose dispensing settings, since unit-dose dispensing practice can be a confounding variable affecting MAE rates. Materials and Methods: We conducted a systematic review of English articles on MEDLINE, EMBASE, EMCARE, CINAHL and Scopus, with the most recent search conducted on 1 February, 2024. Studies were meticulously examined to exclude those with unit-dose dispensing settings.We used ROBINS-I V2 for risk-of-bias assessment and narrative synthesis to summarise findings. Results: We included 4 papers in the review. The categories of MAEs reported among these studies are heterogeneous. Two studies give weak evidence, and 1 study gives moderate evidence that BCMA can lower some categories of MAEs. 1 study gives weak evidence that BCMA increases the wrong administration time error. Discussion: Studies provide weak to moderate evidence that barcode-assisted medication administration can lower certain categories of medication administration errors. However, some reported findings are minimal. Conclusion: More multi-ward, multi-hospital studies need to be conducted to provide stronger evidence on BCMA's impact on MAEs, especially on dosage-related MAEs, in settings without unit dose dispensing practices.

Background: Burnout among healthcare workers (HCWs) is a major occupational health challenge, with detrimental consequences for both staff well-being and patient care. Machine learning (ML) offers potential for early detection and prevention, but evidence synthesis on its predictive performance and applicability is lacking.

Aims: To systematically evaluate the performance, methodological quality, and clinical applicability of ML models for predicting burnout in HCWs.

Design: Systematic review and meta-analysis.

Methods: Ten databases (PubMed, Web of Science, Cochrane Library, Embase, CINAHL, PsycINFO, Scopus, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wanfang) were searched for studies published from inception to 13 February 2025. Eligible studies developed or validated ML models for HCW burnout prediction, using clinically validated tools (e.g. Maslach Burnout Inventory). Two reviewers independently extracted data and assessed study quality using the Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence (PROBAST-AI). Pooled area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated using a random-effects model. Subgroup analyses explored heterogeneity.

Results: Twenty-two studies met inclusion criteria. The pooled AUC was 0.72 (95% CI: 0.68-0.76), indicating moderate discrimination. Sensitivity was 0.63 (95% CI: 0.53-0.73) and specificity was 0.84 (95% CI: 0.75-0.90). Models performed better when using self-reported data, focusing on nurses, conducted in the Asia-Pacific region, or using MBI-based assessments (p < 0.0001). Key predictors clustered into five categories: demographic/occupational, psychological/behavioral, organizational/social, physiological/wearable, and activity/work patterns. All studies showed high or unclear risk of bias in at least one PROBAST-AI domain.

Conclusions: ML models show promise for predicting burnout in HCWs but are limited by methodological weaknesses, heterogeneity, and lack of external validation. Advancing this field requires rigorous design, transparent reporting, multimodal data integration, and ethical safeguards to enable trustworthy clinical use.

Background: A thorough investigation of various medical errors to better understand their underlying causes is the first step toward preventing medication errors and improving patient safety. The causes, types, and rates of medication errors can evolve within the healthcare delivery system. In Korea, systematic data on patient safety incidents remains limited, although healthcare institutions are progressively developing comprehensive information sharing systems.

Objective: This study aimed to analyze factors influencing the risk of adverse events from medication errors in general and tertiary hospitals in Korea using national patient safety statistics (2018-2022).

Methods: A secondary analysis of 8,606 medication error reports was performed to identify risk factors related to hospital and patient characteristics. Firth penalized logistic regression was employed, and sensitivity analyses excluding pandemic years (2020-2021) were conducted.

Results: The risk of adverse events from medication errors was 1.5 times higher in general hospitals with ≥500 beds than in those with <500 beds. Emergency rooms and intensive care units demonstrated the highest risks across both hospital types. Day shifts showed protective effects compared to night shifts in both hospital types. Temporal analysis revealed significantly higher risks in earlier years (2018-2020) compared to 2022, suggesting improvements in medication safety systems. Sensitivity analyses confirmed that pandemic years amplified risks in high-acuity areas, and the protective effect of day shifts was particularly critical during this period. In general hospitals, adult age groups (≥20 years) showed elevated risks, with pediatrics demonstrating 1.7 times higher risk than general surgery.

Conclusions: These findings highlight the need for tailored interventions based on hospital type, location, temporal factors, and patient characteristics. Developing department-specific safety strategies for high-acuity care environments, optimizing staffing patterns particularly during night shifts, implementing age-specific medication management protocols, and continuously improving safety systems are essential to prevent medication errors, enhance patient safety, and improve healthcare outcomes.

Background: Transitioning into intensive care nursing is complex and emotionally demanding for early career nurses. High attrition and psychological stress underscore the need for education that supports resilience and wellbeing alongside clinical competency.

Aim: This paper describes the integration of self-care and wellbeing strategies into a redesigned statewide postgraduate Intensive Care Nursing Transition to Specialty Practice (ICN-TTSP) program in New South Wales, Australia.

Methods: Curriculum mapping aligned the existing ICN-TTSP program with national nursing standards, identify gaps in self-care, resilience, and professional identity. A narrative literature review informed pedagogical strategies, including reflective journaling, role modelling, virtual simulation, and work-integrated learning.

Results: The reimagined ICN-TTSP program embeds self-care and resilience through multimodal educational strategies, including ePortfolio's, video vignettes, and patient/family experience stories. These strategies normalise self-care as professional wellbeing behaviour. Preliminary evaluation shows strong engagement and improved emotional awareness amongst learners.

Conclusion: Embedding wellbeing into postgraduate nursing education is essential for workforce sustainablilty. Further evaluation will assess long-term impact..