Conectar
To critically examine the policy logic of the EU-funded WHO Nursing Action Initiative and assess its capacity to address the structural drivers of Europe's nursing workforce instability, with a specific focus on retention governance as the missing determinant of sustainability.
Although Europe reports high aggregate numbers of nurses, persistent workforce shortages are driven not by insufficient supply but by systemic governance weaknesses that undermine retention. The Nursing Action Initiative provides the first coordinated, multi-country framework aligned with the WHO's 2023–2030 strategic priorities, yet several structural gaps, including the absence of binding retention metrics, enforceable safe staffing standards, harmonized advanced practice pathways, interoperable workforce intelligence, and mandatory accountability, limit its transformative potential. A shift from production-centric policies to a retention-driven governance architecture is therefore essential.
The Nursing Action Initiative represents an important step toward strengthening European nursing workforce policy, but its success will depend on Member States' willingness to implement structural reforms that ensure safe staffing, protect nurses' well-being, expand autonomous practice roles, and stabilize workforce distribution. Without a robust architecture of retention governance, neither the sustainability of Europe's nursing workforce nor the resilience of its health systems can be assured.
This commentary advances the policy debate by framing retention as the central determinant of workforce sustainability. It calls for urgent political commitment to move the Nursing Action Initiative beyond aspirational coordination and toward enforceable, system-level reform capable of delivering lasting improvements in workforce stability and quality of care across the European Union.
Whereas diabetes-related stigma is increasingly recognized as a barrier to diabetes management, little is known about this social phenomenon in collectivist African settings. The purpose of this study was to examine diabetes-related stigma among adults with type 2 diabetes (T2D) in Ghana, highlighting behavioral and psychological mechanisms underpinning the impact of stigma on hemoglobin A1C.
Cross-sectional analytical design.
Adults with T2D (n = 190), seeking care at a tertiary hospital in Ghana, were recruited. A battery of questionnaires assessing psychological (diabetes-related stigma, depression, anxiety, diabetes distress) and behavioral constructs (diabetes concealment and diabetes self-management) were administered. Venous blood samples were obtained for A1C assessment. A latent variable, “adverse psychological outcomes” comprising anxiety, depression, and diabetes distress, was derived and validated using confirmatory factor analysis. Structural equation modeling was used to test multiple psychological and behavioral pathways through which stigma was associated with A1C.
Participants had an average age of 59.44 (SD = 10.7) years, were mostly female (70.5%, n = 134), and had T2D diagnosis for a median of 14.5 years. We found significant indirect effects of T2D stigma on HbA1c through adverse psychological outcomes alone (β = 0.16; 95% CI: 0.01, 0.32, p = 0.038), as well as the combination of adverse psychological outcomes and self-management behaviors (β = 0.16; 95% CI: 0.001 to 0.32, p = 0.048). We also found that the association between T2D stigma and diabetes self-management was fully mediated by adverse psychological outcomes, and participants who conceal their diabetes tend to report greater adverse psychological outcomes.
We note that adverse psychological outcomes play a central role in how T2D stigma is associated with HbA1c. Our findings provide preliminary insight into potential aspects of diabetes that may be targeted in future stigma-reduction interventions.
Our results do provide some indication that addressing mental health issues in individuals with T2D may be an effective intervention strategy in curtailing the adverse clinical effects of T2D stigma. Additionally, our results highlight the importance of incorporating mental health care as part of routine diabetes management in Ghana and other similar African countries where mental health issues are often not prioritized by the healthcare system.
Clinical nurses face notable chronic stress due to work-related stressors, exacerbated by the COVID-19 pandemic, leading to somatic symptoms and low-grade inflammation. Mindfulness meditation has shown promise in reducing stress and improving health outcomes, but its effects on somatic symptoms and inflammatory biomarkers in nurses remain underexplored.
To assess the impact of mindfulness meditation on somatic symptoms and inflammatory biomarkers such as leptin, interleukin-6, and tumor necrosis factor-α among nurses. To explore the secondary effects on perceived stress and trait mindfulness because of the complex interlinked association with the primary outcomes of interest.
A randomized controlled trial was conducted with 102 nurses randomly assigned to a meditation group (8-week mindfulness meditation program) or a non-meditation group. Data were collected using self-report questionnaires (Mindfulness Attention Awareness Scale, Perceived Stress Scale, Patient Health Questionnaire-15) and blood samples for biomarker analysis at baseline and post-intervention.
The meditation group demonstrated notable reductions in perceived stress (p < 0.001), somatic symptoms (p < 0.001), IL-6 (p < 0.001), and leptin levels (p < 0.001) compared to the non-meditation group. Trait mindfulness increased markedly in the meditation group (p = 0.003), while TNF-α levels did not show notable changes.
Mindfulness meditation efficiently reduces perceived stress, somatic symptoms, and inflammatory biomarkers in nurses, highlighting its potential as a holistic intervention to improve both psychological and physical well-being in high-stress healthcare environments.
ClinicalTrail.gove, NCT06635278
Burnout, a form of moral suffering, has become more commonplace among health care workers in recent years. Measures of general resilience have been widely used to capture improvement in burnout but lack the ability to capture the anguish that comes with burnout from a moral standpoint. The purpose of this analysis was to understand whether moral resilience is uniquely related to burnout beyond a measure of general resilience in a sample of interprofessional health care workers.
Secondary analysis of cross-sectional survey data.
In total, 702 interprofessional health care workers participated in a cross-sectional survey. Key measures included the Rushton Moral Resilience Scale (RMRS), the Connor-Davidson Resilience Scale (CD-RISC-10), and the Maslach Burnout Inventory-Human Services Survey (MBI-HSS). Hierarchical multiple regression modeling was used to examine the effect of moral resilience (RMRS) in predicting the three dimensions of burnout (MBI-HSS) over and above general resilience (CD-RISC-10).
Moral resilience explained five, six, and 4% of variance for personal accomplishment, depersonalization, and emotional exhaustion, respectively, after accounting for general resilience (CD-RISC-10) and all covariates.
Findings highlight the clear conceptual differences between general and moral resilience and their unique relationship to burnout. Accounting for moral resilience will facilitate an improved multi-level response to moral suffering among health care workers.
Measuring and understanding the differences between general resilience and moral resilience is vital for us to better facilitate the necessary support(s) for health care workers experiencing moral suffering. This will contribute to more sustainable clinical environments, reduced burnout and suffering, and improved patient outcomes.
This article challenges the tendency to frame diminished confidence and ethical uncertainty among nurses as individual shortcomings. While the need for up-to-date knowledge and moral clarity is undeniable, this piece argues that systemic factors—such as inadequate institutional support, unsafe staffing, and lack of access to continuing education—play a significant role in undermining nurses' ability to act ethically and confidently. Drawing from global case examples, including the Ebola crisis and the COVID-19 pandemic, this article highlights how moral distress often stems not from ignorance or weakness, but from structural barriers and ethical overload.
Smoking is the leading cause of preventable deaths. The training of professionals on brief tobacco interventions (BTIs) increases the effectiveness of these interventions.
To assess the effectiveness of an online training program on BTI based on the 5As and 5Rs model in acquiring anti-tobacco brief advice competencies among nurses.
Quasi-experimental study with a pre-test and post-test design, with a control group and without random assignment. In the experimental group (EG), online training was provided in three sections: BTI theoretical content and methodology, clinical scenario videos, and feedback. Each scenario assessed the 5As and 5Rs as a validated instrument (BTI-Prof(C)). The control group (CG) only assessed the three videos of clinical scenarios. In both groups, competence was measured at the following points in time: T0 (before the training), T1 (at the end of the training), and T2 (after 90 days). The efficacy of the intervention was measured through a two-way ANOVA, and the variation rate was calculated from T0 to T1 and from T0 to T2.
236 nurses participated (157 EG; 79 CG). The mean age was 42.9 years, and 76.7% were women. There was a significant group*time interaction in the three cases, indicating that the online BTI training increases the competence of these professionals in clinical scenario 1 (F = 10.210; p ≤ 0.001; η 2 = 0.081), clinical scenario 2 (F = 6.235; p = 0.002; η 2 = 0.051), and clinical scenario 3 (F = 11.271; p ≤ 0.001; η 2 = 0.090).
A brief, asynchronous, and online intervention using standardized video-based cases is effective in improving nurses' BTI competence. This type of training can be a useful option for the National Health System as part of a global and continuous strategy for nurses to perform BTI.
An asynchronous online training program provides nurses with standardized, evidence-based tools to implement brief tobacco interventions in routine care, offering a scalable and practical solution to strengthen preventive strategies in health systems.
Artificial Intelligence is revolutionizing healthcare by addressing complex challenges and enhancing patient care. AI technologies, such as machine learning, natural language processing, and predictive analytics, offer significant potential to impact nursing practice and patient outcomes.
This systematic review aims to assess the impact of Artificial Intelligence applications in healthcare on nursing practice and patient outcomes. The goal is to evaluate the effectiveness of these technologies in improving nursing efficiency and patient care and to identify areas requiring further research.
This review, conducted in August 2024, followed PRISMA guidelines. We searched PubMed, GOOGLE SCHOLAR, and Web of Science for studies published up to August 2024. The inclusion criteria were original research on AI in nursing and healthcare practice published in English. A two-stage screening process was used to select relevant studies, which were then analyzed for their impact on nursing practice and patient outcomes.
A total of 5975 studies were surveyed from the previously mentioned databases, which met the inclusion criteria. Findings show that AI applications, including machine learning, robotic process automation, and natural language processing, have improved diagnostic accuracy, patient management, and operational efficiency. Machine learning enhanced disease detection, reduced administrative tasks for nurses, NLP improved documentation accuracy, and physical robots increased patient safety and comfort. Challenges identified include data privacy concerns, integration into existing workflows, and methodological variability.
AI technologies have substantially improved nursing practice and patient outcomes. Addressing challenges related to data privacy and integration, as well as standardizing methodologies, is essential for optimizing AI's potential in healthcare. Further research is needed to explore the long-term impacts, cost-effectiveness, and ethical implications of Artificial Intelligence in this field.
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing nursing practices and improving patient outcomes. Tools such as Clinical Decision Support Systems (CDSS), predictive analytics, robotic process automation (RPA), and remote monitoring empower nurses to make informed decisions, optimize workflows, and monitor patients more effectively. AI enhances decision-making, boosts efficiency, and facilitates personalized care, while aiding in early detection and real-time data analysis. It also contributes to better nurse education and patient safety by minimizing errors and enabling remote consultations. However, for AI to be successfully integrated into healthcare, it is essential to tackle challenges related to training, ethical considerations, and data privacy to guarantee its effective implementation and positive impact on the quality and safety of healthcare.
Falls among older adults are a major public health concern, often leading to serious outcomes such as fractures, head trauma, and increased mortality. Virtual reality (VR) interventions have emerged as a promising strategy for fall prevention by improving balance, reducing fear of falling, and enhancing confidence. However, the impact of VR interventions on specific outcomes such as fear of falling, balance, and postural control in older adults remains insufficiently synthesized.
Systematic review and meta-analysis.
A comprehensive systematic search of six databases was conducted from inception to January 20, 2025. Randomized controlled trials (RCTs) evaluating VR interventions targeting fear of falling, balance, and postural control in older adults were included. Methodological quality was assessed using the Cochrane risk-of-bias tool (RoB-2). Pooled standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using random-effects models for each outcome.
Seventeen RCTs involving 988 older adults, published between 2016 and 2025, met the inclusion criteria. VR interventions demonstrated significant effects in reducing fear of falling (SMD = −0.40; 95% CI: −0.72 to −0.08; I 2 = 45.10%; p = 0.02), improving balance (SMD = 0.45; 95% CI: 0.07–0.83; I 2 = 73.54%; p = 0.02), and enhancing postural control (SMD = 0.50; 95% CI: 0.13–0.86; I2 = 46.89%; p = 0.01).
This meta-analysis highlights the effectiveness of VR interventions in reducing fear of falling and improving balance and postural control among older adults.
VR represents a valuable tool in fall prevention strategies, addressing key outcomes essential for maintaining independence and mobility in this population.
Despite evidence supporting nurse-led digitalized diabetes interventions, gaps persist in understanding their specific impact on community-dwelling patients with type 2 diabetes mellitus (T2DM). Prior reviews lacked a quantitative synthesis of these interventions' effects on outcomes like self-care, HbA1c, and quality of life (QoL), limiting their applicability to clinical practice. This study aimed to systematically evaluate and quantify the effectiveness of nurse-led digitalized diabetes management programmes for community-dwelling adults with T2DM.
We searched six databases to identify relevant articles from their inception to June 2024. Randomized controlled trials that evaluate the effects of nurse-led digitalized diabetes management programs for community-dwelling patients with T2DM were included. The Cochrane Risk of Bias tool version 2.0 was used to appraise the included studies. The pairwise meta-analysis was performed through the software Comprehensive Meta-Analysis Version 3.0.
Eleven RCTs were included, encompassing 2943 participants from various regions. Nurse-led digitalized programs significantly improved self-care behaviors (SMD = 1.15; 95% CI: 0.49 to 1.81), and QoL (SMD = 0.65; 95% CI: 0.37 to 0.94). The interventions also demonstrated a clinically meaningful reduction in HbA1c levels (MD = -0.25%; 95% CI: −0.43 to −0.06), highlighting their potential in improving glycaemic control. Heterogeneity across studies was substantial for self-care but moderate for HbA1c and QoL.
Nurse-led digitalised diabetes management programmes effectively enhance self-care behavior, reduce HbA1c levels, and improve QoL among community-dwelling patients with T2DM. These findings underscore the potential of digitalised interventions as scalable and accessible alternatives to traditional diabetes management, particularly in non-institutionalized settings.
Nurse-led digitalised diabetes management programmes can empower community-dwelling patients with T2DM to achieve better health outcomes by enhancing self-care and glycaemic control while improving QoL. Their integration into routine clinical practice could address barriers to care, optimize diabetes management, and reduce the long-term burden of the disease.
The International Prospective Register of Systematic Reviews (PROSPERO) identifier: CRD42024594874
Approximately 25% of the Brazilian population suffers from mental disorders, a prevalence exacerbated by systemic and cultural factors such as socioeconomic inequalities, underfunded mental health services, regional disparities, and persistent stigma. These conditions significantly impact hospital care. Nurses, due to their direct contact with these patients, face challenges ranging from managing physical conditions to handling verbal aggression and psychiatric crises. This study aimed to assess the scientific evidence regarding nursing care for hospitalized patients with psychiatric disorders.
A systematic review with a mixed-methods approach was conducted, registered in PROSPERO (#CRD42022359288) and guided by PRISMA standards. Databases, such as MEDLINE, LILACS, PubMed, Web of Science, Scopus, and BDEnf, were searched using keywords like “Mental disorder,” “Psychiatric health,” “Nursing care,” and “Hospital.” Methodological quality was assessed using JBI and SQUIRE tools. The integration of quantitative and qualitative components occurred through meta-aggregation of qualitative data and frequency-based coding of quantitative themes, allowing thematic convergence across study designs.
Six studies were included. Meta-aggregation revealed frequent terms, such as “Nurse,” “Emergency,” “Screening,” “Patient,” and “Care.” Similarity analysis linked “Nurse” with “perception” and “experience” and “Emergency” with “Screening” and “Mental health,” highlighting the importance of experience and training. Five categories emerged: (1) professional experience (19.05%, showing skill gaps despite experience); (2) caring process (19.05%, stressing efficient screening); (3) barriers and challenges (19.05%, revealing difficulty with comorbidities); (4) training process (19.05%, identifying training deficiencies); and (5) therapeutic interventions (23.81%, discussing restraint use). These percentages refer to the proportional frequency of themes identified across the total number of studies analyzed. For thematic classification, only statistically significant chi-square values (p < 0.05) were considered in the grouping of content.
Nursing care for psychiatric patients in hospitals faces challenges like insufficient training and difficulty managing psychiatric comorbidities. Recommendations include incorporating structured mental health content into nursing curricula and hospital-based continuing education programs. These strategies may guide future healthcare policies in Brazil by improving patient safety, reducing hospital readmissions, and promoting more humane, evidence-based therapeutic interventions.
The findings emphasize the urgent need for targeted education and training to improve nursing care for psychiatric patients in hospital settings.
FreshRSS 