Incidence, knowledge, attitude and practice toward needle stick injury among nursing students in Saudi Arabia

BackgroundNeedle stick injuries constitute the greatest threat to nursing students during clinical practice because of accidental exposure to body fluids and infected blood. The purpose of this study was to (1) determine the prevalence of needle stick injuries and (2) measure the level of knowledge, attitude and practice among nursing students about needle stick injuries.MethodsThree hundred participants undergraduate nursing students at a private college in Saudi Arabia were included, of whom 281 participated, for an effective response rate of 82%.ResultsThe participants showed good knowledge scores with a mean score of 6.4 (SD = 1.4), and results showed that students had positive attitudes (Mean = 27.1, SD = 4.12). Students reported a low level of needle stick practice (Mean = 14.1, SD = 2.0). The total prevalence of needle stick injuries in the sample was 14.1%. The majority, 65.1%, reported one incidence in the last year, while (24.4%) 15 students reported two incident of needle stick injuries. Recapping was the most prevalent (74.1%), followed by during injection (22.3%). Most students did not write a report (77.4%), and being worried and afraid were the main reasons for non-reports (91.2%). The results showed that female students and seniors scored higher level in all needle stick injuries domains (knowledge, attitude and practice) than male students and juniors. Students who had needle stick injuries more than three times last year reported a lower level of all needle stick injury domains than other groups (Mean = 1.5, SD =1.1; Mean = 19.5, SD =1.1; Mean = 9.5, SD =1.1, respectively).ConclusionAlthough the student’s showed good knowledge and positive attitudes in NSI, the students reported a low level of needle stick practice. Raising awareness among nursing students and conducting continuing education related to sharp devices and safety and how to write an incident reporting is highly recommended

Knowledge, attitude, and practice of exclusive breastfeeding among mothers of childbearing age

BackgroundThe American Academy of Pediatrics and the World Health Organization recommend exclusive breastfeeding (EBF) for up to 6 months. Despite the importance of breast milk, EBF is far less prevalent in Nigeria than is recommended for developing countries. Worse still, the odds of EBF practice are very low in rural communities. Hence, the aim of this study was to assess the knowledge, attitude, and practice of EBF as well as identify the factors associated with EBF practice among mothers of childbearing age in Chamo town, Jigawa State, Nigeria.MethodsThe study is a cross-sectional design using a questionnaire to assess the required information. The methodology involved the use of simple random sampling to select mothers of reproductive age from Chamo town, which is a rural community located in Jigawa State, Nigeria. A semi-structured questionnaire was used to assess the mother’s knowledge, attitude, and practices regarding EBF. Simple and multiple logistic regression analyses were performed to determine the factors associated with the practice of EBF.ResultsA total of 400 mothers between the ages of 18 and 41 took part in the study. More than half of the participants (57.8%) were between the ages of 26 and 33 and had a primary level of education (30.5%). Only 26.8% of the respondents practice EBF. Those with a tertiary education (AOR = 10.00, p < 0.001), civil servants (AOR = 12.51, p < 0.001), those aware of EBF (AOR = 3.65, p = 0.002), those with correct EBF knowledge (AOR = 4.61, p < 0.001), those with a positive attitude toward EBF demand (AOR = 0.51, p = 0.050), and those who received encouragement from their community (AOR = 9.87, p < 0.001) were more likely to practice EBF.ConclusionThe findings of the study revealed that the majority of the respondents’ knowledge, attitude, and practice of EBF were minimal. This shows the need to step up efforts to educate mothers about the advantages of EBF for both their own health and that of their children while they are in the hospital recovering from childbirth

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Factors Affecting Patient Safety Culture from Nurses’ Perspectives for Sustainable Nursing Practice

Individual and group beliefs, attitudes, perceptions, competences, and behavioral patterns all contribute to the safety culture of a healthcare company. The study’s goal is to assess nurses’ perceptions of elements that influence patient safety culture in order to promote long-term nursing practice. A descriptive cross-sectional study design was done among a sample of 146 nurses who were recruited from one hospital in Egypt. They completed a self-administered, printed questionnaire. The questionnaire assessed participants’ socio-demographic data and their perception regarding patient safety culture for sustainable nursing practices. The findings revealed that nursing staff had a high perception regarding patient safety culture a with mean score (159.94 ± 7.864). Also, the highest percentage (74.66%) of had no safety events reported yearly. Creating a unit-specific patient safety culture suited to the competences of the unit’s RNs in patient safety practice would be crucial to increasing and sustaining high levels of patient safety attitudes, skills, and knowledge among the unit’s RNs, influencing patient safety. When implementing interventions to promote patient safety and reporting culture in hospitals, policymakers, hospital administrators, and nurse executives should take the current findings into account. A multidimensional network intervention addressing many elements of patient safety culture and integrating different organizational levels should be implemented to enhance patient safety and a no-blame culture

Application of Kano model for optimizing the training system among nursing internship students: a mixed-method Egyptian study

Abstract Background Clinical experience is an important component of nursing education because it translates students' knowledge into practice, which serves as the cornerstone of nursing practice in health care delivery. Purpose The study aims to explore the quality attributes required for optimizing the training system of nursing internship students using Kano model. Methods A concurrent exploratory sequential triangulation design was used for mixed-methods research. A total of 295 nursing internship students (Target Population) were recruited (whole-population sampling) from the study settings in Egypt. Of them, 280 (97.2%) agreed to participate in the study and completed the interview and the self-administered questionnaire. Data collection was done over 6 months from February to August, 2022. Inferential statistics and thematic data analysis were used to analyze the results. Results Findings revealed that there were 35 fundamental attributes required for high-quality nursing students’ internship training. Kano model was used to categorize and prioritize the 35 quality attributes. Kano analysis revealed that 22 attributes were categorized as "attractive" and 11 attributes were as categorized as "must be" and two were indifferent attributes. Conclusion Incorporating the voice of nurse interns during their training is the key to providing efficient and high-quality internship training experience. It could give realistic impressions about the drawbacks of training and proposed solutions. Implications of the study Nurse managers and educators in clinical settings and educational institutions should put much emphasis on the training attributes and pillars to ensure that nursing internship students are mastering the skills of competent alumni. Provision of conducive training environment that fulfill the basic needs of internship students to maintain passion for learning as well as commitment of internship students to nursing profession will improve the satisfaction level and quality of education, training, and practice. Also, incorporating internship students support system with motivation strategies are helpful tools to maintain exemplary performance of internship students during the training period

The Relationship between Nursing Students’ Smart Devices Addiction and Their Perception of Artificial Intelligence

Background: The concept of addiction in relation to cellphone and smartphone use is not new, with several researchers already having explored this phenomenon. Artificial intelligence has become important in the rapid development of the technology field in recent years. It has a very positive impact on our day-to-day life. Aim: To investigate the relationship between nursing students’ addiction to smart devices and their perceptions of artificial intelligence. Methods: A cross-sectional design was applied. The data were collected from 697 nursing students over three months at the College of Nursing, Princess Nourah bint Abdulrahman University. Results: The correlation test shows a significant correlation between smart device addiction and the artificial intelligence of the respondents (p-value < 0.05). In addition, the majority of the students, 72.7% (507), are moderately addicted to smartphones, 21.8% (152) are highly addicted, and only 5.5% (38) have a low addiction. Meanwhile, 83.6% (583) of them have high levels of perception of artificial intelligence and the rest, 16.4% (114), have a moderate level. Conclusions: The nursing students’ perception of artificial intelligence varies significantly according to their level of addiction to smart device utilization

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Abstract The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant

Advanced machine learning techniques for cardiovascular disease early detection and diagnosis

Abstract The identification and prognosis of the potential for developing Cardiovascular Diseases (CVD) in healthy individuals is a vital aspect of disease management. Accessing the comprehensive health data on CVD currently available within hospital databases holds significant potential for the early detection and diagnosis of CVD, thereby positively impacting disease outcomes. Therefore, the incorporation of machine learning methods holds significant promise in the advancement of clinical practice for the management of Cardiovascular Diseases (CVDs). By providing a means to develop evidence-based clinical guidelines and management algorithms, these techniques can eliminate the need for costly and extensive clinical and laboratory investigations, reducing the associated financial burden on patients and the healthcare system. In order to optimize early prediction and intervention for CVDs, this study proposes the development of novel, robust, effective, and efficient machine learning algorithms, specifically designed for the automatic selection of key features and the detection of early-stage heart disease. The proposed Catboost model yields an F1-score of about 92.3% and an average accuracy of 90.94%. Therefore, Compared to many other existing state-of-art approaches, it successfully achieved and maximized classification performance with higher percentages of accuracy and precision

Awareness of reporting practices and barriers to incident reporting among nurses

Abstract Background Adequate incident reporting practices for clinical incident among nurses and even all healthcare providers in clinical practice settings is crucial to enhance patient safety and improve the quality of care delivery. This study aimed to investigate the level of awareness of incident reporting practices and identify the barriers that impact incident reporting among Jordanian nurses. Methods A descriptive design using a cross-sectional survey was employed among 308 nurses in 15 different hospitals in Jordan. Data collection was conducted between November 2019 and July 2020 using an Incident Reporting Scale. Results The participants showed a high level of awareness of the incident reporting with a mean score of 7.3 (SD = 2.5), representing 94.8% of the highest score. Nurses perceived their reporting practices at the medium level, with a mean score of 2.23 out of 4. The main reporting barriers included worrying about disciplinary actions, fearing being blamed, and forgetting to make a report. In regard to awareness of incident reporting, there were statistically significant differences in the mean for total awareness of the incident reporting system scores according to the type of hospital (p < .005*). In regard to self-perceived reporting practices, nurses working in accredited hospitals demonstrated statistically significant differences in self-perceived reporting practices (t = 0.62, p < .005). Conclusions The current results provide empirical results about perceived incident reporting practices and perceived barriers to reporting frequently. Recommendations are made to urge nursing policymakers and legislators to provide solutions for those barriers, such as managing staffing issues, nursing shortage, nurses’ empowerment, and fear of disciplinary actions by front-line nurse managers

An Analysis of Burnout among Female Nurse Educators in Saudi Arabia Using K-Means Clustering

Nurse educators are often burnt out and suffer from depression due to their demanding job settings. Biochemical markers of burnout can provide insights into the physiological changes that lead to burnout and may help us prevent burnout symptoms. Research was conducted using a descriptive cross-sectional survey design and a multi-stage sampling method. The ministry of education website provides a list of Saudi Arabian nursing education programs that offer bachelor of science in nursing programs (BSN). The study consisted of 299 qualified participants. Malsach Burnout Inventory (MBI) was used to measure burnout as the dependent variable. The MBI is a 22-item scale that measures depersonalization, accomplishment, and emotional exhaustion during work. Bootstrapping with 5000 replicas was used to address potential non-normality. During this framework, four deep neural networks are created. They all have the same number of layers but differ in the number of neurons they have in the hidden layers. The number of female nurse educators experiencing burnout is moderate (mean = 1.92 ± 0.63). Burnout is also moderately observed in terms of emotional exhaustion (mean = 2.13 ± 0.63), depersonalization (mean = 2.12 ± 0.50), and personal achievement scores (mean = 12 2.38 ± 1.13). It has been shown that stacking the clusters at the end of a column increases their accuracy, which can be considered an important feature when classifying