Irbid National University

Intimate partner homicide is one of the leading causes of death for women, disproportionately affecting Black women. To investigate the severity of lethality risk and its relationship with individual characteristics, experiences of discrimination, and mental health among urban Black women in Wisconsin since the start of COVID-19. This analysis was part of a larger mixed-methods study. Women were recruited from community agencies in Wisconsin. Eligible participants could participate either in person, by phone call, or via Teams. Lethality was assessed using the Lethality Assessment Protocol (LAP). The experiences of discrimination were measured by the Experiences of Everyday Discrimination (EDS). Mental health and all other demographic variables were self-reported. The data collection was conducted between 2023 and 2025. Analyses were conducted using descriptive analysis, simple linear regression, and multiple regression. All analyses were conducted in SPSS version 29. Data from 185 urban Black women were analyzed, with an overall mean weighted LAP score of 17.29. The overall regression was significant, accounting for approximately 5

BackgroundEducation is one of the many facets of daily life that have been severely interrupted by the ongoing conflict in Palestine since October 7, 2023. Assessing the efficacy and difficulties of electronic distance learning (EDL) requires an understanding of undergraduate nursing students' experiences with it during this period.AimThe study aimed to assess the levels of satisfaction, awareness, attitudes, preferences, and barriers towards EDL distance learning among undergraduate nursing students during the war.MethodsA descriptive cross-sectional design was used. A valid and reliable self-administered questionnaire was utilized to assess the levels of satisfaction, awareness, attitudes, preferences, and barriers toward EDL among undergraduate nursing students.ResultsA total of 292 nursing students participated in this cross-sectional study. Participants demonstrated an overall positive attitude toward EDL (M = 3.02, SD = 0.529), particularly regarding flexibility and ease of use. However, overall satisfaction with EDL was low (M = 2.84, SD = 0.885), revealing a clear contrast between favorable perceptions and actual learning satisfaction. Most students (80.9%) were aware of EDL. Infrastructural barriers were the most prominent challenges, with electricity shortages (64.7%), high internet costs (62.7%), and poor connectivity (61.6%) dominating the EDL experience. Satisfaction levels differed significantly by sex, age group, and digital tool used; however, causal inferences are limited due to the cross-sectional design.ConclusionThe study's findings highlight how crucial it is to continue planning and coming up with solutions that both fill in the gaps in EDL and capitalize on its benefits. The educational institution decision-makers should give this issue a top priority and create a blended learning system to improve communication between instructors and students. Taking into account the security of students during pandemics and wars.

Management of chronic diseases like diabetes mellitus (DM) requires patient activation. Although many studies have associated patient activation with outcomes like increased medication adherence and better blood glucose control, evidence for these outcomes is scarce in the Middle East, particularly in the Jordanian population. To address this issue, the current study examined the association between patient activation and both medication adherence and glycemic control among patients with diabetes in Jordan. A descriptive longitudinal method was used, and convenience sampling was adopted. Newly diagnosed adult patients with diabetes were assessed utilizing the Patient Activation Measure (PAM-13), Adherence to Refills and Medications Scale (ARMS), and glycemic control was evaluated by HbA1c levels. Data were collected from 140 patients at baseline, 3 months, and 6 months from hospital inpatient departments and outpatient clinics. PAM scores increased from 44.7 at baseline to 54.5 at 3 months and 60.0 at 6 months. ARMS scores dropped from 34.4 to 29.7 and 25.3, indicating progressive improvement in medication adherence, and HbA1c from 10.6% to 9.6% and 8.7%. Better activation was significantly associated with higher adherence (F(1, 335.1) = 315.43, p < 0.001) and lower HbA1c (F(1, 362.2) = 414.38, p < 0.001). Time and activation interplay were prominent for adherence (F(2, 177.1) = 3.15, p = 0.045) and HbA1c (F(2, 165.2) = 6.04, p = 0.002). These findings suggest that patient activation is associated with improved health outcomes at 6 months. This finding thus supports prioritizing structured education and support in diabetes care to enhance and maintain patient activation.

PurposeThis study aims to critically assess the maturity and readiness of artificial intelligence (AI) applications in personalized healthcare and to identify the barriers hindering their clinical integration. Design/methodology/approachAdhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a comprehensive analysis of 1,290 peer-reviewed studies sourced from PubMed, Scopus, Google Scholar and IEEE Xplore, spanning the years 2019–2024. The ROBINS-I tool was utilized to evaluate bias, and following a rigorous full-text review, 15 studies met the inclusion criteria. A novel maturity model was developed to categorize the studies. FindingsThe vast majority of studies (13/15) are concentrated at Stage 1 (technical validation) and Stage 2 (clinical efficacy), with a complete absence of studies at Stages 4–5. None have achieved full integration into clinical workflows. We identify a critical “implementation chasm” caused by a trust deficit (black box problem), an equity crisis (algorithmic bias) and a governance gap (data privacy and regulatory challenges). Practical implicationsThe field must shift its focus from technical performance to addressing the systemic barriers identified. We propose a maturity model to guide future research and development towards sustainable and equitable integration. Originality/valueThis review moves beyond cataloging benefits and challenges by introducing a maturity model and critically examining the implementation chasm. It provides a novel framework for advancing AI in personalized healthcare.

Wastewater treatment by electrocoagulation (EC) using waste-derived electrodes is a promising solution, but its viability in irrigation reuse remains unclear. This work presents a quantitative framework integrating agrometeorological analysis (FAO-56 method) and waste information into energy and electrochemical models (Faraday's laws of dissolution) for viability assessment. The circular reuse framework was evaluated at a study site comprising a large institution surrounded by irrigated trees. Onsite experiments showed that electrochemical dosages as low as 20 g/m3 of aluminum (Al) scrap, or 60 g/m3 for iron (Fe) scrap, achieved significant removals (75%-94%) of COD, BOD, TSS, and turbidity. Onsite metal waste generation (593 kg/month) could accommodate ~98,000 m2 of surrounding irrigated lands. Crop production capacities of scrap metals (food per scrap) were estimated to reach 23.3 kg/kg using Al scrap. Based on crop water dynamics, theoretical fixed energy demand for renewable energy integration was calculated to be 0.58 kWh/m3. Validation of model components revealed that modeling scrap electrode consumption comprised minimal uncertainty compared with other components like crop water demand. The reuse framework shows promising potential as a WEFE nexus application but raised some socioeconomic concerns such as impacts on scrap scavengers' livelihoods. Prior to wider adoption, qualitative assessments are necessary to address heavy metals removal, soil contamination, nutrient content, and microbial indicators. Pilot-scale experiments are also encouraged to gain improved insights.