Pressure injuries are increasing worldwide, and there has been no significant improvement in preventing them. This study is aimed at reviewing and evaluating the studies related to the prediction model to identify the risks of pressure injuries in adult hospitalized patients using machine learning algorithms. In addition, it provides evidence that the prediction models identified the risks of pressure injuries earlier. The systematic review has been utilized to review the articles that discussed constructing a prediction model of pressure injuries using machine learning in hospitalized adult patients. The search was conducted in the databases Cumulative Index to Nursing and Allied Health Literature (CINAHIL), PubMed, Science Direct, the Institute of Electrical and Electronics Engineers (IEEE), Cochrane, and Google Scholar. The inclusion criteria included studies constructing a prediction model for adult hospitalized patients. Twenty-seven articles were included in the study. The defects in the current method of identifying risks of pressure injury led health scientists and nursing leaders to look for a new methodology that helps identify all risk factors and predict pressure injury earlier, before the skin changes or harms the patients. The paper critically analyzes the current prediction models and guides future directions and motivations.

The increasing complexity of healthcare delivery and the advancements in medical technology have highlighted the necessity for improved training in nursing education. While traditional training methods have their merits, they often encounter challenges such as limited access to clinical placements, static physical simulations, and performance anxiety during hands-on practice. Virtual reality (VR) has been increasingly adopted for immersive and interactive training environments, allowing nursing students to practice essential skills repeatedly in realistic, risk-free settings. This study presents NursingXR, a VR-based platform designed to help nursing students master essential clinical skills. With a scalable and flexible architecture, NursingXR is tailored to support a variety of nursing lessons and adapt to evolving curricula. The platform has a modular design and offers two interactive modes: Training Mode, which provides step-by-step guided instruction, and Evaluation Mode, which allows for independent performance assessment. This article details the development process of the platform, including key design principles, system architecture, and implementation strategies, while emphasizing its utility and scalability. A mixed-methods evaluation involving 78 participants—both novices and experts—was conducted to evaluate the platform’s usability and user satisfaction. The results underscore NursingXR’s effectiveness in fostering an effective and engaging learning environment as well as its potential as a supplementary resource for nursing training.

Background: The quality and safety education for nurses (QSEN) competency program represents a valuable initiative in nursing practice and education, equipping nurses with the essential knowledge, attitude, and skills (KAS) required to deliver safe, efficient, and patient-centered care. Purpose: This study aims to determine the impact of QSEN competency on the KAS of nurses in Palestine. Method: A quasi-experimental pre-test and post-test design with two groups was used utilizing a questionnaire to collect data from 164 Junior nurses in two governmental hospitals within the period of 25^th, January to the 10^th February 2024. Patricia Benner’s theory suggests that a strong educational foundation and diverse experiences enable nurses to enhance their patient care knowledge and abilities over time. Results: The findings indicate that nurses in Palestine can benefit from targeted interventions and QSEN educational programs aimed at improving their patient-centered care competence, as post-test scores show a significant rise over pre-test scores. Junior nurses who participated in the QSEN program experienced a 57% increase in knowledge, a 57% increase in skills, and a 64% increase in attitudes. The intervention significantly improved knowledge (77.02 vs. 49.19, p < 0.001), quality and safety skills (70.16 vs. 44.61, p < 0.001), and attitudes (75.47 vs. 46.16, p < 0.001) among participants post-procedure, indicating a substantial positive impact on these areas, demonstrating the effectiveness of the educational intervention. Conclusion: The study demonstrates that an educational intervention improves junior nurses’ KSAs for six QSEN competencies, leading to higher average scores in quality and safety competence subscales, thereby enhancing staff satisfaction, and reducing medical errors, and patient safety.

Seroprevalence studies provide an accurate measure of SARS-CoV-2 spread at a population level and the number of undiagnosed individuals. Repeated cross-sectional sero-studies are encouraged to monitor the spread of the virus. The aim of this study is to assess the seroprevalence rate among a random sample of Palestinians residing in the West Bank region of Palestine, especially among those who were not vaccinated and not diagnosed. The study was able to assess the prevalence of asymptomatic cases among the Palestinian adult population. The study also focused on measuring the percentage of adult Palestinians who accepted to get vaccinated across gender and age groups. Methods: This second round cross-sectional study involved 1451 participants, who agreed to be interviewed and answer the questionnaire, where 910 of them agreed to participate in the sero-study and donate a blood sample to be tested for antibodies. The sample was randomly selected from the adult population, 18 years or older, living in the West Bank region of Palestine. Serological tests for 910 adequate serum samples were performed using immunoassays for the detection of antibodies against SARS-CoV-2. Sociodemographic information and medical history data were collected. Results: Study findings indicate that as of October 2021, there was a seroprevalence rate of 75.9% (30% due to infection with COVID-19 virus and 45.9% due to vaccination) with 95% CI (73.1–78.7). The results indicate that the prevalence of antibodies among those who are unvaccinated and undiagnosed was 45.2% with 95% CI (39.9–50.5%). The average age of participants was 37.6 years old. A total of 49.2% were females, and 50.8% were males. In relation to COVID-19, 13.6% of respondents reported getting infected by COVID-19 with statistically significant difference (p-value = 0.001) between males (10.7%) and females (16.5%). In terms of vaccination, 52.8% of respondents reported getting vaccinated with an important difference between males (64.3%) and females (40.9%), (p-value < 0.01). Conclusions: Our findings reveal a drastic rise in seroprevalence of SARS-CoV-2 antibodies due to infection and vaccination. This information is useful for assessing the degree of herd immunity among the adult population and provides better understanding of the pandemic. Population-based seroprevalence studies should be conducted periodically to monitor the SARS-CoV-2 seroprevalence in Palestine and inform policy makers about the efficacy of the surveillance system and the public compliance with vaccination policies especially among females.

Background: Pressure injuries (PIs) are increasing worldwide, and there has been no significant improvement in preventing them. Traditional assessment tools are widely used to identify a patient at risk of developing a PI. This study aims to construct a novel fused multi-channel prediction model of PIs in adult hospitalized patients using machine learning algorithms (MLAs). Methods: A multi-phase quantitative approach involving a case–control experimental design was used. A first-hand dataset was collected retrospectively between March/2022 and August/2023 from the electronic medical records of three hospitals in Palestine. Results: The total number of patients was 49,500. A balanced dataset was utilized with a total number of 1110 patients (80% training and 20% testing). The models that were developed utilized eight MLAs, including linear regression and support vector regression (SVR), logistic regression (LR), random forest (RF), gradient boosting (GB), K-nearest neighbor (KNN), decision tree (DT), and extreme gradient boosting (XG boosting) and validated with five-fold cross-validation techniques. The best model was RF, for which the accuracy was 0.962, precision was 0.942, recall was 0.922, F1 was 0.931, area under curve (AUC) was 0.922, false positive rate (FPR) was 0.155, and true positive rate (TPR) was 0.782. Conclusions: The predictive factors were age, moisture, activity, length of stay (LOS), systolic blood pressure (BP), and albumin. A novel fused multi-channel prediction model of pressure injury was developed from different datasets.

Seroprevalence rates are important indicators to the epidemiology of COVID-19 and the extent of the pandemic given the existence of asymptomatic cases. The purpose of this study is to assess the seroprevalence rate in the Palestinian population residing in the West Bank. Blood samples were collected between 15 ^th June 2020 and 30 ^th June 2020 from 1355 adults from Palestinian households in the West Bank and 1136 individuals visiting laboratories between the 1 ^st May 2020 and 9 ^th July 2020 for a routine checkup. Serological tests for the 2491 serum samples were done using an Immunoassay for the qualitative detection of antibodies against SARS-CoV-2. The random sample of Palestinians living in the West Bank yeilded 0% seroprevalence with 95% CI [0,0.0036], while the lab referrals sample yielded an estimated seroprevalence of 0.354% with 95% CI [0.0011,0096]. Our results indicate that as of July 2020, seroprevalence in Palestine persist low and is inadequate to provide herd immunity, emphasizing the need to maintain health measures to keep the outbreak under control. Population-based seroprevalence studies are to be conducted periodically to monitor the SARS-CoV-2 seroprevalence in Palestine and inform policy makers about the efficacy of their surveillance system.