Background The COVID-19 pandemic imposed unprecedented challenges on healthcare systems worldwide. The pandemic placed frontline nursing staff working in the ICU and ER at the epicenter of this global crisis. This study aimed to assess the multifaceted impact of sociodemographic characteristics on the quality of life (QOL) of nursing staff during the pandemic. Method A cross-sectional survey was conducted to evaluate the impact of sociodemographic characteristics on the QOL of 322 frontline nurses working in the ICU and ER of five Saudi hospitals from May to July 2022. Participants completed the electronic survey questionnaire including demographic characteristics and four domains of QOL from the World Health Organization Quality of Life Questionnaire (WHOQOL-BREFF). The data was evaluated using descriptive and inferential statistics. Results Among 322 nurse participants, the majority were female (84.8%), married (64.4%), and held a bachelor's degree (92.4%). Age (above 40 years), gender (male), and marital status (married) reported a higher individual domain and overall QOL scores which shows that these characteristics have a direct influence on QOL. Years of work experience, extra working hours, and direct contact with COVID-19 patients were additional significant factors. Pearson correlation coefficients among QOL domains ranged from 0.54 to 0.91, indicating a strong interrelation among these domains. The highest transformed score was in the social domain (70.10) while the lowest score was in the psychological domain (59.20). The overall QOL mean score (SD) was 3.49(0.14) and the mean score (SD) of general health was 3.46(0.15). Conclusion The findings of this study suggest that sociodemographic and work-related factors have a complex and multifaceted impact on the QOL of nurses during the COVID-19 pandemic in Saudi Arabia. It also presents an insight into developing specific interventions to enhance nurses' resilience and well-being amidst pandemic challenges and to improve their QOL.

Background: While heart failure with reduced ejection fraction (HFrEF) remains a major global health burden, mesenchymal stem cell (MSC) therapy has emerged as a promising intervention designed to improve cardiac function and reduce morbidity among patients unresponsive to conventional treatments. MSC therapy has shown promise by targeting left ventricular pressure and improving wall thickness, contributing to reductions in HF-related morbidity and mortality rates. This systematic review and meta-analysis bridges a gap in current research through a focused examination of the most recent clinical trials to cohesively assess MSC therapy in HFrEF patients. Methods: We conducted a systematic review and meta-analysis of clinical trials published from 2018 onwards, which were obtained from multiple databases such as PUBMED, Scopus, EBSCO Medline, EBSCO CINAHL Science Direct, and the Cochrane Library. This review investigates the efficacy and safety outcomes of MSC therapy in patients above 18 years of age with a known diagnosis of heart failure with a reduced ejection fraction (HFrEF). The primary outcome was the change in the left ventricular ejection fraction (LVEF). Secondary outcomes encompassed several efficacy outcomes, such as Global Circumferential strain (GCS), the 6-Minute Walk Test (6MWT), Quality of Life (QoL), and major adverse cardiac events (MACE). A PRISMA flow diagram was constructed to illustrate the identification, screening, eligibility, and inclusion of studies at each stage of the review process. Results: A total of 330 studies were initially identified, but only 12 met the inclusion criteria. MSC therapy resulted in a small, non-significant improvement in LVEF (Hedges’ g = 0.096, p = 0.18) with low heterogeneity (I² = 0.5%). Only QoL showed significant improvement (Hedges’ g = −0.518, p = 0.01). No significant changes in other efficacy outcomes were observed. The therapy was not associated with an increased risk of MACE. Conclusion: While MSC therapy was safe and improved QoL for HFrEF patients, it did not significantly improve LVEF or other efficacy outcomes. Further large-scale, standardized trials are required to better understand the potential role of MSCs in heart failure (HF) therapy.

Background and objective: Adverse drug reactions (ADRs) cause substantial harm, and a considerable proportion may be preventable, but under-reporting persists and weakens pharmacovigilance. Spontaneous reporting depends on clinicians, yet under-reporting persists and weakens pharmacovigilance. To map barriers to adverse drug reaction reporting among physicians, a comparison of nurses and pharmacists in single-country studies was carried out between high-income countries (HICs) and low- and middle-income countries (LMICs). Methods: A scoping review was conducted following PRISMA-ScR guidance. PubMed and Web of Science were searched for studies published from 2016 onward. Eligible studies were single-country primary empirical studies including physicians, nurses, or pharmacists and examining ADR reporting. Only barriers that were measured or explicitly explored and reported as extractable results were included. Barriers were coded into 12 domains and summarised by income group and profession. Results: A total of 44 studies were included, with 18 from HICs and 26 from LMICs. Survey designs were most common. Pharmacists were the most frequently studied cadre. Knowledge and training barriers were reported in all studies in both income groups. Fear of legal or punitive concerns was reported in 13/18 (72.2%) HIC studies and 17/26 (65.4%) LMIC studies. Time and workload barriers were reported in 10/18 (55.6%) HIC studies and 11/26 (42.3%) LMIC studies. Access barriers to tools, forms, and information technology showed the clearest income group difference: these were reported in 5/18 (27.8%) HIC studies versus 16/26 (61.5%) LMIC studies. Lack of feedback or acknowledgement was reported in 8/18 (44.4%) HIC studies and 10/26 (38.5%) LMIC studies. Conclusions: Barriers extend beyond individual knowledge in all settings. The main income group difference was the greater prominence of reporting system access barriers in LMICs compared with workflow and time pressure barriers in HICs. Addressing fear and building a supportive non-punitive reporting culture remains a cross-cutting priority because these were common issues in both income groups and can limit reporting even when infrastructure and training exist.

Objective. To introduce PsiBench, a clinically validated medication-safety benchmark for evaluating large language models (LLMs) against the standards used to certify hospital computerized provider order entry (CPOE) and electronic health record (EHR) systems, and a non-overlapping three-tier evaluation framework separating highest-stakes discrimination, the operational CDS regime, and category-correct alerting. Materials and Methods. PsiBench comprises 492 medication-safety scenarios across 11 safety categories, created by clinical pharmacology experts whose work underpins an annualized testing procedure used by more than 2,000 U.S. hospitals. The three-tier framework partitions the scenarios non-overlappingly: Discrimination (98 scenarios, 50 fatal vs 48 deception, near-balanced 51%/49%); Operational (394 scenarios, 261 serious unsafe plus 133 safe including 41 Excessive Alerts reclassified as operational negatives); and Attribution (311 alert-required scenarios). We evaluated 40 frontier LLMs from 10 providers over 3 runs per scenario at temperature 0.2 (or the provider default where temperature is not configurable), yielding 59,040 evaluations conducted April 21-23, 2026. Results. Headline binary performance on the full benchmark spans a wide range across the 40 models: F1 78.5%-92.3%, accuracy 65.4%-89.8%, sensitivity 81.4%-100.0%, specificity 6.1%-81.8%. Leading models by F1 (o4-mini 92.3%; o3 92.2%) pair high sensitivity with meaningful specificity; three models saturate sensitivity at 100% but fall below 25% specificity, indistinguishable from a naive always-alert classifier. The wide spread on a single headline metric motivates tier-specific analyses, developed in a separate clinical paper. Discussion and Conclusion. PsiBench and the three-tier framework operationalize a rigorous evaluation rubric for LLM medication safety, grounded in two decades of national hospital audit experience. The framework generalizes to any binary medication-safety classifier (rule-based, conventional ML, or LLM-driven), supporting tier-aware model selection and post-deployment surveillance.

BACKGROUND Medication errors are among the most common and preventable causes of hospital patient harm. Computerized provider order entry (CPOE) and electronic health record (EHR) systems with clinical decision support (CDS) are the primary technological response, yet hospital medication safety remains uneven, both in catch rates for genuinely fatal orders and in alert volumes that drive alert fatigue. Large language models (LLMs) are now entering production medication-management workflows, but existing LLM medication-safety evaluations typically test isolated subtasks on small or synthetic datasets, assess only one or two models, and report a single aggregate accuracy or F1 metric on a class-imbalanced scenario set, an approach that obscures sensitivity-specificity tradeoffs and inflates apparent performance on the dominant class. OBJECTIVE To introduce PsiBench, a clinically validated medication-safety benchmark for evaluating large language models (LLMs) against the standards used to certify hospital CPOE and EHR systems, together with a non-overlapping three-tier evaluation framework that separates highest-stakes discrimination, the operational CDS regime, and category-correct alerting. METHODS PsiBench comprises 492 medication-safety scenarios across 11 safety categories, created by clinical pharmacology experts whose work underpins an annualized testing procedure used by more than 2000 US hospitals. The three-tier framework partitions the scenarios non-overlappingly: Discrimination (98 scenarios; 50 fatal versus 48 deception, near-balanced 51%/49%); Operational (394 scenarios; 261 serious unsafe plus 133 safe, the latter including 41 Excessive Alerts reclassified as operational negatives); and Attribution (311 alert-required scenarios). We evaluated 40 frontier LLMs from 10 providers over 3 runs per scenario at temperature 0.2 (or the provider default where temperature is not configurable), yielding 59,040 evaluations conducted April 21-23, 2026. RESULTS Headline binary performance on the full benchmark spanned a wide range across the 40 models: F1 78.5%-92.3%, accuracy 65.4%-89.8%, sensitivity 81.4%-100.0%, and specificity 6.1%-81.8% (Youden's J 0.06-0.75). Leading models by F1 (o4-mini 92.3%; o3 92.2%) paired high sensitivity with meaningful specificity, whereas three models saturated sensitivity at 100% but fell below 25% specificity, making them indistinguishable from a naive always-alert classifier. This wide spread on any single headline metric demonstrates that single-metric evaluation collapses three clinically distinct questions into one number and motivates the tier-specific analyses developed in a separate clinical paper. CONCLUSIONS PsiBench and the three-tier framework operationalize a rigorous evaluation rubric for LLM medication safety, grounded in two decades of national hospital audit experience. The framework generalizes to any binary medication-safety classifier (rule-based, conventional machine learning, or LLM-driven) and supports tier-aware model selection and postdeployment surveillance. A high benchmark score demonstrates capability on a curated scenario set but does not by itself guarantee performance on a specific hospital population or formulary; prospective local validation and ongoing post-market surveillance remain necessary before clinical deployment.