Search Results (5,131)

Pain management in multiple trauma patients presents a complex clinical challenge due to competing priorities such as hemodynamic instability, polypharmacy, coagulopathy, and the urgency of life-saving interventions. In this context, peripheral nerve blocks (PNBs) are increasingly recognized as a valuable asset for their role in managing pain in patients with multiple traumatic injuries. By reducing reliance on systemic opioids, PNBs support effective pain control and facilitate early mobilization, aligning with enhanced recovery principles. This narrative review summarizes current evidence on the use of PNBs in the context of polytrauma, focusing on their analgesic efficacy, integration within multimodal analgesia protocols, and contribution to improved functional outcomes. Despite these advantages, clinical application is limited by specific concerns, including the potential to mask compartment syndrome, the risk of nerve injury or local anesthetic systemic toxicity (LAST), and logistical barriers in acute trauma settings. Emerging directions in the field include the refinement of ultrasound-guided PNB techniques, the expanded use of continuous catheter systems, and the incorporation of fascial plane blocks for anatomically complex or multisite trauma. Parallel efforts are focusing on the development of decision-making algorithms, improved risk stratification tools, and integration into multimodal analgesic pathways. There is also growing emphasis on standardized clinical protocols, simulation-based training, and patient education to enhance safety and consistency in practice. As evidence continues to evolve, the long-term impact of PNBs on functional recovery, quality of life, and healthcare utilization must be further explored. With thoughtful implementation, structured training, and institutional support, PNBs may evolve into a cornerstone of modern trauma analgesia. Full article

Background/Objectives: Older adults (65+) are the fastest growing age group in Canada, comprising 18.8% of the country’s population. During the COVID-19 pandemic, use of virtual care, including telehealth and tele-medicine, increased dramatically among older adults in Canada who often face higher health risks, mobility limitations, and many barriers to accessing healthcare. Despite the rapid expansion in virtual care, no systematic review has focused specifically on virtual care among older adults in Canada. This review aims to explore the factors influencing virtual care adoption and the experiences of older Canadians during the pandemic through a systematic review. Methods: Conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, the review involved a comprehensive search of PubMed, Scopus, ESCBOHost, and Web of Science on 2 May 2025, yielding 281 unique citations. After screening and applying eligibility criteria, 15 studies employing quantitative, qualitative, or mixed-methods designs, with sample sizes ranging from 15 to 2,282,798, were included and appraised using the Mixed Methods Appraisal Tool (MMAT). Results: The review identified three domains of factors and the ways in which each factor shapes older adults’ virtual care experiences: (1) personal factors influencing virtual care use and demand (e.g., age, education, language, income, immigration status, community sizes), (2) resource factors impacting virtual care adoption (e.g., technology access, support), and (3) varying virtual care experiences among older adults (e.g., in assessment and communication efficacy, privacy, care quality, convenience, safety, and costs). Conclusions: This review highlights the complexities of virtual care engagement among older adults and underscores the need for inclusive, tailored strategies to improve the accessibility and effectiveness of virtual care delivery in both pandemic and post-pandemic contexts. Full article

This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model’s predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013–2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R² = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application—followed by experimental verification—allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m³, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning. Full article

Background: Patient safety culture is critical for enhancing the quality and safety of healthcare. Studies in low- and middle-income countries have reported challenges in developing patient safety culture, especially in implementing nonpunitive responses to errors and event reporting. However, evidence from Laos remains limited. Objectives: This study aimed to assess patient safety culture in hospitals in southern Laos, using a validated survey tool to identify strengths and areas of improvement. Methods: A cross-sectional study using purposive sampling was conducted in four provincial and twenty-three district hospitals in southern Laos. Healthcare workers on patient safety committees responded to the Hospital Survey on Patient Safety Culture. The positive response rate was analyzed. Bivariate tests (chi-square/Fisher’s exact) were applied to compare positive response rates between hospital types and professions. Results: A total of 253 valid responses (75.5%) were analyzed. “Organizational Learning–Continuous Improvement” scored over 75% in both provincial and district hospitals. In contrast, “Nonpunitive Response to Error” and “Frequency of Events Reported” were scored <20% on average. Provincial hospitals scored significantly higher than district hospitals in supervisory support and handoffs. Conclusions: This study illustrated strengths in organizational learning while identifying nonpunitive responses and event reporting as critical areas of improvement for hospitals in Laos. To improve patient safety, hospitals in Laos must promote a culture in which errors can be reported without fear of blame. Strengthening leadership support and reporting systems is essential. These findings can inform strategies to enhance patient safety in other low-resource healthcare settings. Full article

Depression represents one of the most prevalent mental health disorders globally, significantly impacting quality of life and posing substantial healthcare challenges. Traditional diagnostic methods rely on subjective assessments and clinical interviews, often leading to misdiagnosis, delayed treatment, and suboptimal outcomes. Recent advances in biosensing technologies offer promising avenues for objective depression assessment through detection of relevant biomarkers and physiological parameters. This review examines multi-modal biosensing approaches for depression by analyzing electrochemical biosensors for neurotransmitter monitoring alongside wearable sensors tracking autonomic, neural, and behavioral parameters. We explore sensor fusion methodologies, temporal dynamics analysis, and context-aware frameworks that enhance monitoring accuracy through complementary data streams. The review discusses clinical validation across diagnostic, screening, and treatment applications, identifying performance metrics, implementation challenges, and ethical considerations. We outline technical barriers, user acceptance factors, and data privacy concerns while presenting a development roadmap for personalized, continuous monitoring solutions. This integrative approach holds significant potential to revolutionize depression care by enabling earlier detection, precise diagnosis, tailored treatment, and sensitive monitoring guided by objective biosignatures. Successful implementation requires interdisciplinary collaboration among engineers, clinicians, data scientists, and end-users to balance technical sophistication with practical usability across diverse healthcare contexts. Full article

Autism Spectrum Disorder (ASD) remains significantly underdiagnosed in women, resulting in a persistent gender gap with important clinical, functional, and psychosocial implications. This narrative review explores the multifactorial barriers contributing to diagnostic disparities, including the male-oriented structure of current diagnostic criteria, the prevalence of co-occurring psychiatric conditions, and the phenomenon of social camouflaging shaped by culturally reinforced gender norms. These factors frequently lead to delayed identification, clinical misinterpretation, and suboptimal care. The review synthesizes evidence from clinical, psychological, and sociocultural research to demonstrate how the under-recognition of ASD in women impacts mental health outcomes, access to education, occupational stability, and overall quality of life. Special emphasis is placed on the consequences of missed or late diagnoses for healthcare delivery and the educational needs of clinicians involved in ASD assessment and care. This article concludes with actionable, evidence-based recommendations for enhancing diagnostic sensitivity, developing gender-responsive screening strategies, and integrating training on female autism presentation into medical and allied health education. Addressing these challenges is essential to reducing diagnostic inequities and ensuring timely, accurate, and person-centered care for autistic women throughout their lifespan. Full article

Epileptic seizure prediction remains a critical challenge in neuroscience and healthcare, with profound implications for enhancing patient safety and quality of life. In this paper, we introduce a novel seizure prediction method that leverages electroencephalogram (EEG) data, combining discrete wavelet transform (DWT)-based time–frequency analysis, advanced feature extraction, and deep learning using Fourier neural networks (FNNs). The proposed approach extracts essential features from EEG signals—including entropy, power, frequency, and amplitude—to effectively capture the brain’s complex and nonstationary dynamics. We measure the method based on the broadly used CHB-MIT EEG dataset, ensuring direct comparability with prior research. Experimental results demonstrate that our DWT-FS-FNN model achieves a prediction accuracy of 98.96 with a zero false positive rate, outperforming several state-of-the-art methods. These findings underscore the potential of integrating advanced signal processing and deep learning methods for reliable, real-time seizure prediction. Future work will focus on optimizing the model for real-world clinical deployment and expanding it to incorporate multimodal physiological data, further enhancing its applicability in clinical practice. Full article

This study aims to understand the meaning nursing professionals attribute to their lived experiences of providing humanized care within a public hospital setting. Grounded in Jean Watson’s theory of human caring, the research adopts a qualitative, descriptive phenomenological design to capture the perceptions and emotions of nurses regarding humanized care. Data were collected through semi-structured interviews with nine experienced nurses, selected through purposive sampling. The interviews, conducted virtually between July and December 2024, were analyzed using Colaizzi’s method and supported by Atlas.ti software. Four main thematic categories emerged: institutional health policies, professional image and identity, strengths and challenges in care, and essential competencies for humanized care. The findings highlight the critical role of empathy, cultural sensitivity, ethical commitment, and emotional presence in delivering compassionate care. Participants emphasized that, beyond clinical procedures, humanized care requires relational and contextual sensitivity, often hindered by institutional limitations and excessive administrative burdens. The study concludes that nursing professionals are key agents in promoting ethical, empathetic, and culturally respectful practices that humanize health services. These insights offer valuable contributions for designing policies and training strategies aimed at strengthening humanized care as a cornerstone of quality healthcare systems. Full article

Workplace violence (WV) is a ubiquitous risk in healthcare settings where it has been associated with physical and mental health problems. We aimed to investigate the relationship between the violence experienced by healthcare workers (HCWs) and the presence of eating disorders (EDs). During routine health surveillance, 1215 HCWs were questioned about their experience of WV and the short version of the Eating Disorder Examination Questionnaire (EDE-QS) was used to assess their eating behaviors. Sleep quality, stress, and the presence of common mental illnesses and metabolic disorders were also evaluated. HCWs who had experienced one or more assaults in the previous year had a significantly higher EDE score than their colleagues. In a multivariate model, WV doubled the risk of EDs (odds ratio 2.33, confidence intervals 95% 1.30; 4.18, p < 0.01). A very significant association was observed between common mental disorders and EDs (OR 1.13, CI 95% 1.04; 1.23, p < 0.01), while low sleep quality almost reached a significant level (OR 1.09, CI 95% 0.99; 1.20). The higher frequency of EDs among workers subjected to violence may result from maladaptive coping mechanisms used when stress and mental health problems caused by WV lead to compensatory overeating. However, reverse causation, where WV is induced by stigmatization, cannot be ruled out. Because of the considerable impact EDs have on physical and mental health, productivity, and patient care, healthcare organizations should adopt programs designed to prevent these disorders in HCWs. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article

Background: Moral harassment (mobbing) in healthcare, particularly among nurses, remains a persistent issue with detrimental effects on mental health, resilience, and quality of life. Aim: We examine the relationship between the resilience of nurses working in Emergency Departments (EDs) and how these factors influence experiences of workplace mobbing. Methods: This cross-sectional study included 90 nurses from four public hospitals in Greece’s 5th Health District. Data were collected between October 2023 and March 2024 using the WHOQOL-BREF, Workplace Psychologically Violent Behaviors (WPVB) scale and the Connor–Davidson Resilience Scale (CD-RISC). The sample consisted primarily of full-time nurses (84.3% female; mean age = 43.1 years), with 21.1% reporting chronic conditions. Most participants were married (80.0%) and had children (74.4%), typically two (56.1%). Statistical analyses—conducted using SPSS version 27.0—included descriptive statistics, Pearson and Spearman correlations, multiple linear regression, and mediation analysis, with significance set at p < 0.05. Results: Resilience was moderate (mean = 66.38%; Cronbach’s α = 0.93) and positively correlated with all WHOQOL-BREF domains—physical, psychological, social, and environmental (r = 0.30–0.40)—but not with the overall WHOQOL-BREF. The mean overall WHOQOL-BREF score was 68.4%, with the lowest scores observed in the environmental domain (mean = 53.76%). Workplace mobbing levels were low to moderate (mean WPVB score = 17.87), with subscale reliabilities ranging from α = 0.78 to 0.95. Mobbing was negatively associated with social relationships and the environmental WHOQOL-BREF (ρ = –0.23 to –0.33). Regression analysis showed that cohabitation and higher resilience significantly predicted better WHOQOL-BREF outcomes, whereas mobbing was not a significant predictor. Mediation analysis (bootstrap N = 5000) indicated no significant indirect effect of resilience in the relationship between mobbing and WHOQOL-BREF. Conclusions: Resilience was identified as a key protective factor for nurses’ quality of life in emergency care settings. Although workplace mobbing was present at low-to-moderate levels, it was negatively associated with specific WHOQOL-BREF domains. Enhancing mental resilience among nurses may serve as a valuable strategy to mitigate the psychological effects of moral harassment in healthcare environments. Full article

Background: Health equity can only be achieved when every individual has access to quality healthcare without fear of being discriminated against. This study analyzed the sociodemographic characteristics associated with self-reported racial discrimination when receiving medical care in the United States. Methods: This quantitative cross-sectional study utilized the 2022 National Trends Survey 6. We performed a logistic regression analysis using 6102 survey responses from study participants who answered the question about perceived discrimination. Results: Older adults aged 75 years and above had significantly lower odds of reporting perceived discrimination when receiving medical care compared to those aged 18–34 years (AOR = 0.24; 95% CI: 0.10–0.58). The odds of reporting perceived discrimination were significantly higher among non-Hispanic Blacks (AOR = 7.30; 95% CI: 4.48–11.88), Hispanics (AOR = 3.56; 95% CI: 2.45–5.17), non-Hispanic Asians (AOR = 5.95; 95% CI: 2.25–15.73), and individuals identifying as non-Hispanic Other (AOR = 10.91; 95% CI: 5.42–21.98), compared to non-Hispanic Whites. Compared to individuals from households earning less than USD 20,000, the odds of reporting perceived discrimination when receiving medical care were significantly lower among individuals from households earning between USD 50,000 and <USD 75,000 (AOR = 0.42; 95% CI: 0.23–0.78) and those earning USD 75,000 or more (AOR = 0.43; 95% CI: 0.22–0.83). Conclusions: Despite having a multicultural and ethnically diverse population, racial discrimination persists in the United States and has become a barrier to achieving health equity. Health organizations should implement policies that ensure health workers attend mandatory anti-racism training. Full article

Background/Objectives: Artificial intelligence (AI) is rapidly emerging as a transformative force in healthcare education, including orthodontics. This systematic review and meta-analysis aimed to evaluate the integration of AI into orthodontic training programs, focusing on its effectiveness in improving diagnostic accuracy, learner engagement, and the perceived quality of AI-generated educational content. Materials and Methods: A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Embase through May 2025. Eligible studies involved AI-assisted educational interventions in orthodontics. A mixed-methods approach was applied, combining meta-analysis and narrative synthesis based on data availability and consistency. Results: Seven studies involving 1101 participants—including orthodontic students, clinicians, faculty, and program directors—were included. AI tools ranged from cephalometric landmarking platforms to ChatGPT-based learning modules. A fixed-effects meta-analysis using two studies yielded a pooled Global Quality Scale (GQS) score of 3.69 (95% CI: 3.58–3.80), indicating moderate perceived quality of AI-generated content (I² = 64.5%). Due to methodological heterogeneity and limited statistical reporting in most studies, a narrative synthesis was used to summarize additional outcomes. AI tools enhanced diagnostic skills, learner autonomy, and perceived satisfaction, particularly among students and junior faculty. However, barriers such as limited curricular integration, lack of training, and faculty skepticism were recurrent. Conclusions: AI technologies, especially ChatGPT and digital cephalometry tools, show promise in orthodontic education. While learners demonstrate high acceptance, full integration is hindered by institutional and perceptual challenges. Strategic curricular reforms and targeted faculty development are needed to optimize AI adoption in clinical training. Full article

Background and Aim: Type 2 diabetes (T2D) continues to pose a significant public health challenge worldwide. Among therapeutic options, glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have proven effective in optimizing glycemic control and improving cardiometabolic profiles. Semaglutide, now available in an oral formulation, represents a modern strategy to improve patient adherence while supporting glucose and weight regulation. This study primarily investigated the effects of oral semaglutide on key metabolic indicators and secondary endpoints included cardiovascular risk markers (blood pressure and lipid profile) and patient-reported quality of life (QoL). Study Design and Methods: A longitudinal, prospective observational study was conducted involving patients with T2D across two Italian healthcare facilities. Participants were assessed at baseline (T0) and at three subsequent intervals—6 months (T1), 12 months (T2), and 18 months (T3)—following the initiation of oral semaglutide use. Key Findings: Out of 116 participants enrolled, 97 had complete and analyzable data. Across the 18-month follow-up, significant improvements were observed in glycemic parameters, with a notable reduction in HbA1c levels (T0 vs. T3, p = 0.0028; p ≤ 0.05, statistically significant). Self-reported outcomes showed enhanced quality of life, especially in treatment satisfaction and perceived flexibility (T0 vs. T3, p < 0.001). Conclusions: Daily administration of 14 mg oral semaglutide in individuals with T2D resulted in substantial benefits in glycemic regulation, weight reduction, cardiovascular risk management, and overall patient satisfaction. These findings reinforce its potential role as a sustainable and effective option in long-term diabetes care from both a clinical and public health perspective. Full article