The British Journal of Nursing Studies (ISSN: 2755-080X) is an international, double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. The journal provides a forum for original research and scholarship about health care delivery, organization, management, workforce, policy and research methods relevant to nursing, midwifery and other health related professions. Topics cover not only the field of nursing but also related fields such as psychology, education, management and statistics.
The Impact of Leadership Styles on Job Satisfaction among Omani Nurses: A Systematic Review
The systematic literature review proposed will analyze the impact of leadership styles on job satisfaction of nurses in Oman and other countries of the GCC. Nurses job satisfaction is a critical field of concern in improving the quality of care to patients, turnover rates, and establishment of a productive working environment. The review states that transformational and servant leadership played a significant role in enhancing the job satisfaction of nurses when compared to transactional and laissez-faire leadership styles (Alkhateeb et al., 2025; Notarnicola et al., 2024). Inspirational and highly intellectual transformational leadership, which is based on personal consideration, was the most effective type of leadership that fostered nurse involvement and satisfaction (Ali Ibreekaw, 2022). Servant leadership that centered on satisfying the followers was also positively related to increased job satisfaction, reduced burnout, and retention rates (Alkhateeb et al., 2025). In addition, the review identifies the key intermediaries in this connection, including empowerment and organizational commitment, which were found to moderate the connection between leadership and job satisfaction (Al Maqbali, 2015). The need to empower the nurses and allow them to experience some form of autonomy and satisfaction was also essential due to shared decision-making and professional growth opportunities (Ali Ibreekaw, 2022). Organizational commitment was also a significant factor that helped to retain nurses and enhance their job satisfaction (Alkhateeb et al., 2025). The review concludes with the recommendations to the healthcare organization of Oman, and the GCC, which should be directed at the creation of a leadership program, supporting transformational and servant leadership styles, encouraging empowerment and ensuring an efficient working environment. The future research must review the long-term consequences of leadership styles and their connection with the other organizational variables (Alrawahi et al., 2024). The review provides practical suggestions on the means of improving the satisfaction and retention of nurses in the health facilities in the GCC region.
Nurse job satisfaction, leadership styles, transformational leadership, servant leadership, organizational commitment, empowerment, healthcare management, GCC countries, nurse retention, job satisfaction mediators, Oman, healthcare leadership, burnout reduction, leadership development, work environment
Copyright (c) 2026 British Journal of Nursing Studies
A Deep Learning Framework for Early Breast Cancer Detection Among U.S. Women: Integrating Mammography and Clinical EHR Data
Breast cancer is the most commonly diagnosed malignancy and a leading cause of cancer-related mortality among women in the United States (American Cancer Society, 2024). Although screening mammography has reduced mortality, its performance is limited by lower sensitivity in women with dense breasts, inter-reader variability, and frequent false-positive recalls (Skaane, 2019). At the same time, widespread adoption of electronic health records (EHRs) has created an opportunity to combine imaging with rich clinical information for more accurate, individualized risk assessment. This study proposes a deep learning framework for early breast cancer detection among U.S. women that integrates digital mammography with structured EHR variables. Mammography images are obtained from a curated public dataset such as the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), which provides biopsy-verified benign and malignant cases (Lee et al., 2017). EHR-style data are organized following the structure of large U.S. clinical datasets, for example MIMIC-IV, and include demographics, breast density, reproductive and hormonal factors, comorbidities, family history, and prior screening history (Johnson et al., 2023). A convolutional neural network extracts high-level image features, which are fused with gradient-boosted clinical embeddings for malignancy prediction and compared with image-only and EHR-only baselines. In this practice framework, the multimodal model demonstrates superior discrimination and a better balance between missed cancers and false positives, particularly in women with dense breasts, highlighting the potential of integrating mammography and EHR data to support earlier detection and more informed, risk-adapted clinical decision-making.
Copyright (c) 2025 British Journal of Nursing Studies
A Multilevel Perspective on PrEP Adherence in HIV Prevention: An Integrative Review Guided by the Socioecological Model
Pre-exposure prophylaxis (PrEP) remains one of the most effective strategies for preventing HIV infection, yet its success depends on sustained adherence. Evidence increasingly shows that adherence lapses are not purely behavioral but stem from interacting biological, psychological, and social factors. This review aimed to synthesize existing evidence on how hazardous alcohol use, post-traumatic stress disorder (PTSD), and gut microbiome dysbiosis interact as syndemic determinants of PrEP adherence, using the Socioecological Model (SEM) to interpret multilevel influences.An integrative review was conducted following Whittemore and Knafl’s (2005) five-stage framework—problem identification, literature search, data evaluation, data analysis, and presentation. Systematic searches were performed in PubMed, PsycINFO, Scopus, and Web of Science for studies published between 2016 and March 2025. Inclusion criteria covered peer-reviewed, English-language human studies linking PrEP adherence with alcohol use, PTSD, or microbiome disruption. Two reviewers independently screened, extracted, and appraised data for methodological rigor and conceptual relevance. Findings were integrated thematically and interpreted across SEM levels.Fifteen studies met inclusion criteria. Hazardous drinking (55–70%) and PTSD (28–43%) were independently associated with reduced adherence, with combined exposure decreasing adherence from >80% to approximately 54–60%. Odds ratios for nonadherence ranged 1.8–3.2. Microbiome dysbiosis, particularly reduced Lactobacillus and elevated inflammatory markers, was linked to gastrointestinal intolerance and discontinuation. Six recurring themes reflected syndemic reinforcement across biological, psychological, and structural domains.Adherence to PrEP is shaped by interconnected mechanisms rather than isolated behaviors. Future longitudinal and mixed-methods studies should evaluate trauma-informed, microbiome-supportive, and nurse-led interventions, grounded in the Socioecological Model, to achieve sustainable and equitable HIV prevention.
HIV prevention; PrEP adherence; socioecological model (SEM); alcohol misuse; post-traumatic stress disorder; gut microbiome; integrative review
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
Deployment of a National Health Risk Surveillance Dashboard: An Interactive Platform for Visualizing Cancer and Kidney Disease Trends in the United States
The United States is faced with a great threat of increasing incidence of long term conditions like cancer and kidney diseases. Despite the wealth of data in terms of health, there is still a gap of turning that data into useful information that can be used by the policy makers, health care providers and researchers alike. The above-mentioned gap was the motivation behind the development of the interactive National Health Risk Surveillance Dashboard that will present the long-term trends in the prevalence of cancer and kidney diseases in the states of the United States from 2001 up to 2021. The study is dedicated to the implementation of a National Health Risk Surveillance Dashboard with an option to visualize cancer and kidney disease dynamics across the United States. In the country, morbidity and the leading cause of mortality and health expenditure remains chronic diseases, especially cancer and kidney disease. Public health surveillance plays a very important role in keeping track of whether or not there is disease prevalence, the populations at risk and can lead to policy making. The classical health data reporting systems are usually faulty in their data coherence, unavailability, and inability to provide interactive visualization. Providing the solutions to these problems, this paper used the United States Chronic Disease Indicators (CDI) 2023 dataset, and organized the data of years 2001 to 2021 with the aim to conduct the analysis comprehensively. The research methodology included the extraction, cleaning, and preprocessing of the data point and statistical analysis through Python and the Tableau application to allow the development of interactive dashboards reflecting longitudinal, demographic, and geographical trends. These results indicate the increasing trends of the prevalence of cancer and kidney diseases, large regional disparities, gender, and age variations along with different data confidence levels. The created dashboard is a convenient system that allows policymakers, healthcare providers, and researchers to access, research, and analyze data about chronic diseases. It can be used in making evidence-based decisions, providing assistance in public health implementation and also fostering transparent reporting of health data. Another point that the study highlights is that in future improvements of the dashboard, it might be interesting to consider integrating predictive analytics and machine learning and real-time updates to the data. The study establishes that an interactive, data-driven surveillance platform can considerably enhance the monitoring of chronic disease, resources appropriation, and creation of policies, which can help enhance the health outcomes of the country. This research recommends the constant promotion and use of these kinds of technology tools in the field of health monitoring to respond to the changing difficulties in the medical services of the United States.
Chronic Diseases Surveillance, Health Risks Dashboard, Cancer trends, Kidney disease analysis, Public health analysis display, US health analytics
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/
A Predictive AI Framework for Cardiovascular Disease Screening in the U.S.: Integrating EHR Data with Machine and Deep Learning Models
Cardiovascular disease (CVD) is the leading global cause of death, with over 18 million fatalities annually. Early and accurate diagnosis is essential to reduce its clinical and economic impact. This study presents an AI-driven framework for the early detection of CVD using structured data from electronic health records (EHRs). The Cleveland Heart Disease dataset was used to train and evaluate multiple supervised machine learning models, including Logistic Regression, Random Forest, SVM, KNN, and XGBoost. Comprehensive preprocessing steps were applied, such as feature normalization, missing value imputation, and one-hot encoding. Model performance was assessed using precision, recall, F1-score, and ROC-AUC, with XGBoost achieving the highest ROC-AUC score of 0.91. To support clinical interpretability, we employed feature importance analysis, ROC curves, and confusion matrices. The study confirms the potential of interpretable AI models to enhance diagnostic accuracy, facilitate early interventions, and integrate seamlessly into clinical decision support systems for proactive healthcare delivery.
Artificial Intelligence for Chronic Kidney Disease Risk Stratification in the USA: Ensemble vs. Deep Learning Methods
This research provides the application of machine learning and deep learning techniques in early detection of chronic kidney disease (CKD) using clinical data which are commonly collected in U.S. healthcare settings. CKD, a progressive condition marked by declining kidney functions, poses a major public health challenges in the U.S due to CKD’s prevalence and the high cost of treatment in after stages. By utilizing a dataset comprising 24 clinical parameters from 400 individuals 250 of whom were diagnosed with chronic kidney disease the research emphasizes the critical need for early and accurate prediction to update patient outcomes and minimize the burden on the healthcare system. The methodology of the research included data preprocessing, imputation of missing values of the CKD, and strategic feature selection, which are followed by the implementation of various machine learning algorithms such as K-Nearest Neighbors and Gradient Boosting, beside it deep learning models including Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN). Among these, Gradient Boosting emerged as the most effective approach, achieving an impressive 97% accuracy in predicting CKD status in healthcare system in U.S. Its performance highlights the potential of machine learning in identifying key diagnostic features of CKD and also offering a suitable solution for early intervention in clinical practice across the whole U.S.
Chronic Kidney Disease (CKD), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Deep Learning, Machine Learning, Medical Diagnosis, Data Preprocessing, U.S. Healthcare.
Prevalence and Determinants of Unmet Need for Modern Contraception and Family Planning Counseling Among Postpartum Women
Unmet need for modern contraception among postpartum women remains a significant public health challenge despite increased access to services. In Saudi Arabia, limited research has specifically addressed this issue. This study aimed to assess the prevalence and correlates of unmet need for modern contraception and family planning counseling among postpartum women in Riyadh. A cross-sectional descriptive study was conducted at Prince Sultan Military Medical City in Riyadh. A total of 357 postpartum Saudi women aged 15–49 were recruited using a convenience sampling method. Data were collected through a validated, Arabic-translated questionnaire. Statistical analysis included descriptive statistics, Chi-square tests, and logistic regression to identify factors significantly associated with unmet contraceptive need. The prevalence of unmet need for modern contraception among postpartum women was 63%. Significant associations were found with the number of children and the intention to delay pregnancy. Despite moderate awareness, only 29.5% were currently using contraception. Fear of side effects, lack of counseling, and reliance on lactational amenorrhea were common reasons for non-use. High levels of unmet need highlight the urgency for improved postpartum family planning counseling in Saudi Arabia. Targeted educational interventions and inclusion of partners in counseling may enhance contraceptive uptake. Structured programs should address cultural, informational, and access-related barriers to reduce unintended pregnancies.
Postpartum contraception, Unmet need, Saudi Arabia
Advancing Diabetic Retinopathy Detection with AI and Deep Learning: Opportunities, Limitations, and Clinical Barriers
Diabetic retinopathy (DR) remains one of the leading causes of preventable blindness globally, particularly among individuals with long-standing diabetes. Early detection through regular eye examinations is essential to prevent irreversible vision loss associated with advanced stages, such as proliferative diabetic retinopathy and diabetic macular oedema. Although screening programs have been successfully deployed in various healthcare systems, rising diabetes prevalence places a growing strain on medical infrastructure. As a result, there is a critical need for scalable, automated diagnostic tools. Recent advances in artificial intelligence, particularly deep learning using convolutional neural networks (CNNs), offer promising solutions for automated analysis of retinal images. These models have demonstrated high diagnostic performance in identifying DR stages and detecting macular oedema in imaging modalities like optical coherence tomography (OCT). Several AI algorithms have now received regulatory approval and are gradually being adopted in clinical workflows. Furthermore, innovations in portable imaging devices open new avenues for patient-led monitoring and remote diagnostics. However, despite their potential, current mobile imaging systems often fall short in achieving the resolution and consistency required for reliable DR detection when compared to standard fundus photography. Integration into telemedicine platforms could bridge this gap by enabling remote screening and centralized analysis, yet real-world implementation remains limited. Challenges such as legal regulations, software interoperability, and misalignment with existing national screening protocols continue to hinder widespread adoption. This paper explores the current state of AI-assisted diabetic retinopathy screening, evaluates the readiness of emerging technologies, and discusses key barriers that must be addressed to enable global deployment and improve patient outcomes.
Diabetic Retinopathy (DR), Deep Learning, Convolutional Neural Networks (CNNs), Retinal Image Analysis, Telemedicine, Diabetic Macular Oedema (DMO), Optical Coherence Tomography (OCT).
Is there an Effective Delivery of Information in Cancer Patients Participating in Clinical Trials?
Information is vital to ensure the delivery of effective, patient-centred and quality care. However, healthcare professionals especially those working in clinical trials setting face a lot of barriers that inhibit effective delivery of information. This is because the role of a Clinical Research Nurses (CRN) is slightly different from the traditional nursing role and the challenges these nurses face may be specific to this group. This papers amis to to analyse the role of information in the management of cancer and how it relates to effectiveness of care, and critically analyse the role of a CRNs and the barriers they face in effectively delivering information to patients participating in clinical trials. Literature search was carried out, looking that the role of information in delivery of quality care. Literature relating to role of information in influencing the patient’s experiences and the role of the Multi-Disciplinary Team in the delivery of information was reviewed. The review highlighted the importance of information delivery in impowering patients to self-manage not just the disease, but treatment related adverse events as well. The review also highlighted the importance of MDT collaboration to ensure that there is a wide range of expertise that will enable provision of comprehensive patient care and the challenges Clinical Research Nurses face in ensuring delivery of information. Information delivery is vital in ensuring quality care and measures should be put in place to ensure that this is achieved. Clinical research nurses play a vital role in facilitating the delivery of clinical trials and ensuring that patients receive effective patient care. However, the barriers they face in ensuring delivery of information which empowers patients are mainly due to the nature of their role.
Information delivery, clinical trials, multidisciplinary team working, clinical research nurses.
Early Detection of Breast Cancer Using Machine Learning: A Tool for Enhanced Clinical Decision Support
Breast cancer arises when there is an abnormal increase in breast tissue, resulting in the creation of lumps or irregular cell layers. This cancer ranks as the second most common among women worldwide, trailing only melanoma, and primarily impacts those over 50 years old, although it can manifest at any age. Timely identification and robust preventive measures are essential for minimizing health risks associated with cancer. Clinical trials in cancer prevention are persistently investigating innovative approaches for early diagnosis and treatment. This research utilizes machine learning methods to categorize breast cancer tumours as benign or malignant, facilitating prompt clinical decision-making. The dataset utilized for this study was obtained from Kaggle and underwent preprocessing and exploratory data analysis, incorporating correlation matrices and Principal Component Analysis (PCA) for dimensionality reduction and data visualization. Four supervised machine learning algorithms were assessed: Decision Tree, Logistic Regression, Bagging, and Random Forest. The evaluation of the models was conducted using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and Area Under the Curve (AUC). The Random Forest classifier demonstrated the highest accuracy at 98%, showcasing its exceptional ability to manage the provided dataset effectively. Furthermore, the Bagging algorithm exhibited the highest AUC value at 99%, underscoring its effectiveness in differentiating between benign and malignant cases. The results indicate that ensemble techniques, especially Random Forest and Bagging, serve as valuable instruments for breast cancer classification, potentially aiding clinicians in achieving early and precise diagnoses.
Heart Disease Risk Prediction Using Machine Learning: A Data-Driven Approach for Early Diagnosis and Prevention
Cardiovascular diseases continue to be a major cause of death worldwide and a major challenge to healthcare systems in both the developing and developed world. In the US alone, nearly a fifth of all deaths in a year are caused by cardiovascular diseases, which imposes a huge burden on public and economic resources. The chief aim of this work was to create and rigorously test machine learning models that are effective in the prediction of heart disease risk for various populations. Based on well-annotated datasets and well-labeled variables like age, systolic/diastolic blood pressure, cholesterol level, type of chest pain, and electrocardiogram results. We used the publicly accessible Cleveland Heart Disease data for this study on Heart Disease Risk Prediction Using Machine Learning. The data consisted of 303 patient records and 14 important attributes typical for cardiovascular health: age, sex, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, and ST depression caused by exercise, among others. The target variable marks the presence or absence of heart disease as labeled in the data using five categories, later binarized for classification purposes (1 = disease, 0 = no disease). To develop a strong predictive model for the identification of people vulnerable to heart disease, three established supervised classification algorithms have been adopted: Logistic Regression, Random Forest Classifier, and XG-Boost Classifier (Extreme Gradient Boosting). To determine the accuracy and reliability of the designed machine learning models for heart disease risk prediction, a battery of evaluation metrics was utilized that presented distinct insights into model performance. The XG-Boost model had a substantial training accuracy, followed very closely by a high test accuracy, which indicated good generalization to the unseen test data. The deployment of machine learning-based heart disease risk prediction models in preventive care represents a major push in the U.S. public healthcare sector. These models can easily be implemented within electronic health record systems utilized in clinics, hospitals, and primary care to automatically indicate high-risk individuals using real-time clinician data. Machine learning-driven heart disease prediction models also have transformative value in remote monitoring of health and telemedicine, which have emerged as big trends in the U.S., particularly in the aftermath of the COVID-19 pandemic. One of the key strengths of machine learning models is that they can provide customizable risk scores that are attuned to the multifaceted demographic profile of the United States. As machine and AI technologies continue to mature, there is increasing potential to expand their use to predict not only heart disease but also associated comorbid conditions such as stroke, metabolic syndrome, chronic kidney disease, and type 2 diabetes.
Phenomenologizing The Life After Nursing Practice of Registered Nurses: A Transcendental Approach
The nursing profession has always played a key role in promoting health and preventing illness. However, they are not immune to facing issues in their everyday lives. These issues have played a vital role in the decision of professional nurses to leave the profession and seek work in other industries to satisfy their needs. Professional nurses stop nursing practice and opt to stay in non-healthcare professions. Hence, this study aims to provide empirical insights on the lives of registered nurses after nursing practice. The researchers utilized a qualitative research design, specifically transcendental phenomenology by Clark Moustakas (1994). The data were gathered using a self-made questionnaire with eight participants from San Pablo City, Laguna, who were selected through the non-probability purposive sampling technique. The transcripts were analysed thematically using the Interpretative Phenomenological Analysis and NVivo application, and six major themes were derived, namely Transition and Personal Growth, Advancing Career and Cultivating Values, Dynamics of Interpersonal Relationships in Career Transition, Evolution of Interpersonal Skills and Strategies, Adaptation to New Environment and Challenges, and Reflection on Career Path and Identity. The results suggest that changing careers offers opportunities for enhancing professional abilities as individuals adapt to their new environments, leveraging skills acquired from previous roles.
Life After Nursing Practice, Transcendental Phenomenology
Leadership and Employee Cultural Perceptions: A Study of West African Migrant Nurses
Studies suggest that migrant nurses from the black and minority ethnic (BME) groups working in the United Kingdom have variously reported negative experiences at work. To learn more about the nature of these experiences, this study explored in detail the experiences of a purposive sample of fifteen (N-15) West African migrant nurses working in selected independent nursing homes in the UK. Data collected was managed and analysed using the interpretative phenomenological analysis (IPA) process to focus on the nurses’ in-depth perceptions through lived experiences on interactions and with managers and colleagues from a different cultural background to further interpret and meanings the nurses attach to these interactions and relationships. Findings suggest the nurses’ sensitivity to managerial and leadership styles at their workplaces. These sensitivities were observed in relation to identified four elements of behaviours and processes at work thematically noted as follows: manager-subordinate relations, channels for the communication of authority, management of organisational processes and age-related values. There are indications of high value significances the nurses attached to the power distance and collectivist cultural values which are consistent with their aboriginal value orientations which informed perceptions and interpretations of experiences at work. The findings have important implications for understanding the organisational leadership styles adjustments needed for the effective management of the well-being of migrant workers in destination countries for optimal engagement. Further practical suggestions include ways of supporting migrant nurse employees in adjusting to destination countries cultural practices at work while also recognising and validating their attachments to cherished aspects of their own indigenous cultural values, especially in multicultural sensitive society such as the United Kingdom.
Organisational leadership, Cultural orientations, Interpretative phenomenological Analysis (IPA), West African migrant nurses, United Kingdom.
Development of Competency Framework for Nurses in Hospital-at-Home in Singapore
Mobile Inpatient Care at Home (MIC@Home), also known as Hospital-at-Home (HaH), was introduced in Singapore to enable treatment modalities traditionally done in the hospital to be carried out in the comfort of patients’ own homes. Management of higher acuity patients in the HaH setting requires nurses to have a wide range of skills from both inpatient and community. The purpose of the study is to develop a competency framework to guide the training curriculum of HaH nurses to ensure sustained quality and safety of care of patients. The six-step model for developing competency framework was applied and a Delphi process was then employed to ensure the validity of the framework by seeking feedback and consensus from a panel of experts from both clinical and nursing backgrounds. A competency framework was developed, acknowledging the limitations of the small panel of experts surveyed and also the possible overgeneralisation due to the patients’ acuity, complexities and specialties of our HaH team. We have proposed a competency framework that will serve as an important playbook for hospitals in equipping HaH nurses with the necessary tools, resources and knowledge to ensure the standard of care for HaH patients.
Impact of Homecare Physical therapy on Parkinson’s patients to improve functional mobility
Parkinson's disease (PD) is a neurodegenerative disorder that significantly impairs functional mobility due to symptoms such as bradykinesia, muscle rigidity, and postural instability. Homecare physical therapy (HPT) has emerged as a viable intervention to enhance mobility and quality of life for individuals with PD. This review examines the efficacy of HPT in improving functional mobility, muscle strength, balance, and overall physical activity levels in Parkinson’s patients. HPT programs, involving tailored exercises for flexibility, strength, and balance, can lead to significant improvements, reducing the risk of falls and promoting functional independence in the individual. The flexibility of home-based sessions provides a comfortable and accessible environment, encouraging adherence to exercise regimens. Additionally, HPT allows for individualized treatment plans, addressing specific patient needs and progression of symptoms. Despite the positive outcomes, further research is needed to optimize exercise protocols and assess long-term benefits. Overall, HPT represents a promising approach to managing functional mobility challenges in Parkinson’s disease, potentially enhancing the overall well-being and autonomy of affected individuals.
Parkinson’s disease, Homecare physical therapy, Physical function, Fall prevention, Functional mobility
Introducing Research into Practice: Establishing the Evidence-based Moving on Programme as Part of Standardized Cancer Care
The Moving On Programme (MOP) was developed as a specific intervention to fill an identified service need. This programme was piloted and analyzed by previous research and this paper outlines the process and challenges of introducing the MOP to become part of standardized cancer care. Any adaptations that were required due to the global COVID-19 pandemic restrictions are outlined. Health economic analysis outlines the cost of the MOP and participants’ evaluations are presented. MOP has the potential to be far reaching in its appeal and could easily be transferred to other regions.
AI-Based Brain MRI Segmentation for Early Diagnosis and Treatment Planning of Low-Grade Gliomas in the USA
The detection of brain tumors in the USA is a complex task that requires high accuracy from imaging modalities. While it's true that many early-stage brain tumors can be managed effectively, they are often more aggressive and more challenging to treat than their higher-grade counterparts, ultimately leading to a fatal outcome with an average survival time of just 7 years after diagnosis. Therefore, these types of tumors must be accurately identified from MRI images, which are the most effective tool for diagnosing brain abnormalities. We have developed two deep-learning convolutional neural network models, U-Net and DeepLab, to segment brain MRI scans. We apply image segmentation techniques, which cluster the parts of the brain images into tumor or nontumor areas. To assess the effectiveness of our segmentation algorithm, we employ a widely recognized and reliable measure known as the Dice coefficient. The Dice coefficient objectively assesses the similarity between the predicted segmentation results and ground truth data. Using the Dice coefficient, we can better understand how well our algorithm captures the complex nuances of the image data. Our dataset is a valuable resource for brain MRI segmentation tasks. It comprises images from The Cancer Imaging Archive (TCIA), which provides high-quality imaging of 110 patients with lower-grade gliomas included in the broader data collection from The Cancer Genome Atlas (TCGA). These patient-specific brain MRI scans are accompanied by manually created fluid-attenuated inversion recovery (FLAIR) masks, allowing for detailed segmentation and abnormality detection.
Copyright (c) 2023 British Journal of Nursing Studies
Risk Factors of Cardiovascular Diseases among Medical Staff in Saudi Arabia
Healthcare workers have an increased risk of developing cardiovascular diseases as a work-related disease and also due to the sedentary lifestyle, unhealthy behavior, and occupational stress to which they are exposed. This study investigates the prevalence and risk factors of CVDs among medical staff at Prince Sultan Military Medical City, Riyadh, Saudi Arabia. A cross-sectional study was conducted among 291 medical staff at Prince Sultan Military Medical City, Riyadh, using stratified random sampling. Data were collected through the WHO STEPS questionnaire, physical measurements (BMI, blood pressure), and biochemical assessments (lipid profiles, glucose levels). Statistical analyses, including logistic regression, were performed using SPSS version 26 to identify the prevalence and determinants of CVD risk factors. Ethical approval was obtained, and all participants provided informed consent.The mean age of participants was 35 years, with 20% being smokers and 50% insufficiently active. Obesity (50%), hypertension (30%), and diabetes (10%) were prevalent clinical risk factors. High LDL cholesterol (30%) and low HDL cholesterol (20%) were observed. Key predictors of CVD included hypertension (OR = 3.20), diabetes (OR = 2.80), obesity (OR = 2.50), and high stress levels (OR = 1.60). This study underscores the need for workplace health interventions targeting modifiable CVD risk factors among healthcare workers in Saudi Arabia.
Cardiovascular disease, healthcare workers, risk factors, Saudi Arabia
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/
Health Promoting Lifestyle Behaviours among Older Adults in Saudi Arabia: Implications and Future Directions
The older adult population is rapidly increasing worldwide, with significant healthcare needs, especially in Saudi Arabia. Health-promoting behaviors are crucial to enhance quality of life and reduce morbidity among older adults. This study aimed to assess the level of health-promoting lifestyle behaviors among older adults in Saudi Arabia and identify the factors associated with these behaviors.A quantitative cross-sectional study was conducted among 304 older adults attending primary healthcare centers in Saudi Arabia. Data were collected using the Health-Promoting Lifestyle Profile II (HPLP II) and analyzed using SPSS version 26, with multiple logistic regression employed to determine factors affecting health-promoting behaviors. The overall mean score of health-promoting behaviors was 162.7 out of 208, indicating moderate engagement. Spiritual growth had the highest mean score, while physical activity had the lowest. Marital status, education, and the presence of chronic kidney and pulmonary diseases were significantly associated with better health-promoting behaviors. Older adults in Saudi Arabia exhibit moderate levels of health-promoting behaviors, with spiritual growth being the most prominent. Targeted interventions are needed to enhance physical activity and nutrition, focusing on vulnerable groups to improve overall health outcomes.
Health, Promoting, Lifestyle, Behaviors, Older adults
Visualizing Wound Healing: Integrating Clinical Imaging with Nursing Practices for Enhanced Outcomes
Wound healing (WH) is an extraordinary biological process that encompasses the management of injuries, tissue repair, and the restoration of the integrity of the skin and surrounding tissues. This process plays a crucial role as a defensive barrier, preventing infections and injuries and maintaining homeostasis. A normal wound progresses through a series of stages, including hemostasis, inflammation, proliferation, and remodeling, within a specific timeframe. This type of wound is known as an acute wound. However, when the healing process is delayed or interrupted at any stage, the wound becomes chronic. Several factors can influence WH, such as physiological factors like age, gender, and oxygen levels; pathological conditions such as diabetes, cancer, and obesity; and lifestyle factors including poor hygiene, smoking, and alcohol consumption. If a chronic wound is not properly managed, it can lead to further complications, such as an increased risk of infection, higher morbidity, and even mortality. The role of healthcare, particularly the nurse, in wound care is to monitor the case, handle it, and reduce complications. Therefore, it is essential to optimize nursing practices and ensure adherence to guidelines, keeping up with their continuous updates. Visualizing WH is a crucial aspect of wound care, enabling effective monitoring and evaluation of the healing process. This review explores the theoretical perspectives and practical applications of clinical imaging in wound assessment. Techniques such as 3D scanning, ultrasound imaging, and visual documentation are highlighted for their role in tracking wound characteristics, diagnosing complications, and guiding treatment plans. Emphasis is placed on integrating advanced technologies with nursing practices to enhance patient outcomes. By combining theoretical frameworks with innovative imaging methods, this study underscores the importance of visual tools in improving the understanding and management of WH.
Wound healing, nursing, clinical imaging, 3D scanning, ultrasonography
The Impact of Shift Work on Nurses' well-being and Patient Care Excellence in Inpatient Settings
The reviewed document extensively examines the impact of shift work (SW) on nurses’ health and patient care quality in inpatient settings. Shift work, especially night shifts, has been associated with significant disruptions to nurses’ circadian rhythms, leading to adverse outcomes such as sleep disturbances, chronic diseases, psychological stress, and decreased cognitive function. These health challenges correlate with diminished patient care quality, manifesting in errors, poor communication, and delayed treatments. The study emphasizes the necessity of addressing these issues through organizational policies and interventions. Strategies such as optimized scheduling, stress management, and adequate support systems are proposed to mitigate the negative effects of SW, ensuring nurse well-being and sustained healthcare quality.
shift work, nurses, health, patient care quality, inpatient settings
Revisiting Diabetes with a Focus on Complications, Innovations, and Care Strategies
Diabetes mellitus (DM) is a chronic endocrine health issue characterized by persistent hyperglycemia resulting from impaired insulin excretion, insulin impact, or both. This condition poses significant challenges due to its associated complications, including macrovascular conditions like heart disease and stroke, as well as microvascular complications such as diabetic neuropathy, retinopathy, nephropathy, and cardiomyopathy. Specialist nurses are integral to comprehensive diabetes care, offering expertise that spans multiple domains. Their contributions include improving inpatient management by reducing hospital stays and enhancing clinical outcomes, providing outpatient support to foster self-management and monitor disease progression, and delivering specialized care in pediatric diabetes, diabetic foot ulcer prevention, and gestational diabetes management. Additionally, nurse-led interventions, including education programs, telehealth services, and telephone support, empower patients to better manage their condition (table 1). This paper also highlights the challenges specialist nurses face, such as limited scope of practice, inadequate remuneration, and the need for greater interprofessional collaboration. By addressing these barriers and leveraging the expertise of specialist nurses, healthcare systems can significantly enhance diabetes management, reduce complications, and improve the overall quality of life (QOL) for individuals with diabetes. The findings underscore the critical role of specialist nurses in addressing the global diabetes epidemic and the importance of supporting their continued involvement in patient-centered care.
Diabetes mellites, diabetic complications, diabetes nurse specialists, diabetic care
Quality Of Care From The Patient's Perspective and Related Factors
This study will help clarify the current status of care quality at Hoan My Dong Nai International Hospital, from the patient's perspective, and identify influencing factors, in order to propose solutions to improve the quality of nursing services, better meeting the needs of patients. A cross-sectional descriptive study was conducted on 286 patients hospitalized for at least 72 hours at a private hospital in Vietnam. The participants were selected from the inpatient departments of the hospital. The quality of care was evaluated as very good, with an average score of 3.43 ± 0.55. The technical capacity of healthcare and the material-technical conditions were rated as good. Aspects such as identity-oriented approach and the socio-cultural environment were assessed as very good. The length of hospital stay, cleanliness of the room and bathroom, quiet environment in the evening, and the patient's self-reported health status had a strong to very strong relationship with the quality of care. This study has provided important information about the current status and factors influencing the quality of care from the patient's perspective. This information will help healthcare managers develop and implement solutions to improve the quality of nursing services and better meet the needs of patients.
Quality of nursing care; patient expectations; patient outcomes
Copyright (c) 2024 British Journal of Nursing Studies
Predicting and Monitoring Anxiety and Depression: Advanced Machine Learning Techniques for Mental Health Analysis
Anxiety and depression are considered among the most prevailing mental illnesses; they affect millions in the USA and worldwide. Besides being highly prevalent, these conditions have major implications for individuals and American society as a whole. The prime objective of this research project was to design and evaluate advanced machine learning methodologies for the monitoring and prediction of anxiety and depression. The rise in recent advances in Machine Learning and AI technologies has unleashed tremendous potential in the diagnosis and monitoring of mental health conditions such as anxiety and depression. Predictive models, powered by Machine Learning algorithms, process vast amounts of data and detect patterns that might have evaded human clinicians. This dataset for the current research project was retrieved from the website kaggle.com and shared publicly with anyone by the Harvard Data Verse repository. The dataset contained behavioral, psychophysiological, and demographic data that were collected from 593 participants aged 18-35 years for the prediction of anxiety and depression disorder risk. For this study, three machine learning algorithms were deployed: Logistic Regression, XG-Boost, and Random Forest. To assess and evaluate the performance of the algorithms, two key performance evaluation metrics were utilized MSE & R-squared. By reviewing the performance of the aforementioned three machine learning models, Linear Regression, Random Forest Regressor, and XG-Boost Regressor, using evaluation metrics MSE and R-squared are compared in a tabular form. Retrospectively, all three models performed remarkably well, with very low MSE values and R-squared values close to 1. Linear Regression marginally outperformed the others, but all models were successful in predicting the anxiety or depression indicator accurately. The proposed models are valid and reliable models for predicting mental health, therefore enabling the identification of at-risk individuals well in advance, allowing early intervention to prevent symptom onsets or advancements in their course and thus improve overall outcomes.
Mental Health, Machine Learning, Anxiety Prediction, Depression Detection, Advanced Algorithms
An Extended Literature Review on Factors Linked to Occupational Stress among Nurses in the Emergency Department and its Impact on Job Effectiveness
Occupational stress among nurses, stemming from high work demands and limited empowerment, negatively impacts job satisfaction and performance, potentially compromising patient care and safety. The study aims to assess the factors linked to occupational stress among nurses in the emergency department and its impact on job effectiveness. This literature review explores factors influencing occupational stress in emergency room nurses, impacting safety, satisfaction, and retention. Strategies for managing stress in A&E settings are discussed, emphasizing mental wellbeing, psychological support, and organizational reforms. Studies highlight the profound effects of stress on nurses' quality of life and patient outcomes, underscoring the need for effective intervention and support systems in emergency departments. This analysis aimed to delve into factors contributing to occupational stress among ED staff and its impact on job performance while identifying potential resolutions. Through a comprehensive literature survey and critical examination, three significant themes emerged in this domain.
Occupational stress, nurses, emergency department, job effectiveness, literature review
Advancing Heart Disease Prediction through Machine Learning: Techniques and Insights for Improved Cardiovascular Health
Heart disease persists as one of the leading causes of death in the USA and worldwide, accounting for a substantial proportion of global mortality. The significance of early detection of heart disease lies in its capability to counter catastrophic events such as strokes and heart attacks, which are often irreversible and fatal. Machine learning algorithms are gradually revolutionizing heart disease prediction since they can handle complex, multi-dimensional data sets. This research project used the Cleveland dataset from the UCI Machine Learning Repository, containing 70,000 records of patients with 12 unique features. Three machining learning algorithms were trained: Logistic Regression, Random Forest, and Support Vector Machines. Each algorithm was evaluated for precision, accuracy, recall, F1-score, and ROC-AUC. Based on the proof of the evaluation metrics for Logistic Regression, Random Forest, and SVM. In that respect, Logistic Regression was the best overall model since it yielded the highest ROC-AUC score, balancing true positives and false positives better than the rest of the models. The Support Vector Machine had the best accuracy, although it performed similarly to Logistic Regression but slightly lower. In retrospect, the implications for heart disease prediction are evident with simple algorithms such as Logistic Regression affirmatively performing better in specific early heart detection tasks, especially when balancing precision and recall. Indisputably, Machine learning models will have a high clinical impact on heart disease prediction since they enable early detection of heart diseases, leading to timely interventions and better patient prognoses.
Heart Disease prediction; Early detection; Cardiovascular improvement; Logistic Regression; Random Forest; Support Vector Machines.
Prevalence and Risk Factors of Fibroma among General Hospitals in Iraq
Benign tumours made of fibrous or connective tissue are called fibromas. Malignant tumours are referred to as fibrosarcomas. A clinical entity that can develop in the oral cavity is an oral fibroma. The objective of this research was to ascertain the fibroma prevalence and risk factors in Iraqi general hospitals from 2018 to 2023. In this survey, a prospective cross-sectional research was conducted. Randomly selected genders who were at least 15 years old were chosen using a systematic sampling technique. The study included a standardised self-administered questionnaire to gather personal and historical data from the participants. Out of 106 patients comprised, 55 (51.90%) of females and 51 (48.10%) of males, with a mean age of (43.59 ± 18.36) years old. However, fibroma was statistically associated with topography at (P-Value=0.004). The prevalence of fibroma diagnosed among general hospitals aged 15 to 98 years old. Fibromas often form in adults between the ages of 40 and 60. A variety of factors, such as trauma or localised irritation on the tongue, lip, gums, or inside of the cheeks, might contribute to their development.
Fibrosarcma, benign tumors, fibroma, oral fibroma, trauma, tongue.
Knowledge and Attitude about In Vitro Fertilization (IVF) among People in Saudi Arabia
Knowledge; Attitude; In Vitro Fertilization; Saudi Arabia
The Development and Effectiveness of Leadership and Management Program for Enhancing the Nursing Managers Competency: A Quasi-Experimental Pre-Posttest Study
Nurse managers are vital in managing their workplace and providing high-quality, safe nursing care. Health organizations require qualified managers with leadership and management knowledge and skills to provide healthy work environments and enhance the outcomes related to staff nurses and patients. It is imperative to offer nursing managers an educational program about leadership and management competencies to ensure their readiness to accomplish their roles. Developing leadership and management competency intervention programs is critical to improving the nurse managers' competency and effectiveness and driving team and organizational performance. This study aimed to develop and evaluate the impact of a leadership and management competency intervention program among front- and second-line nurse managers. A quasi-experimental design with one group. The study involved 48 front-line and second-line nursing leaders from 25 health institutions, including hospitals and primary healthcare facilities. Self-assessment questionnaires on leadership competency and effectiveness, as well as a program satisfaction scale, were used. The data was analyzed using SPSS version 25 and represented in percentage, mean, and standard deviation. Statistical significance among variables was considered at a p-value less than 0.05. The research findings suggest that a leadership and management training program had a notable impact on the development of nurse managers. The difference between the nurses' pre-and post-test scores and managerial competency levels was statistically significant (Z=-4.201, p<0.05). Specifically, the mean score increased from 2.03 in the pre-test to 2.40 in the post-test, indicating a significant improvement. The improvement was particularly pronounced in operational succession planning, delegating roles, managing staff shortages, staff performance, conflict resolution, change process, and emotional intelligence and communication. Notably, competencies related to operational plan, key performance indicators and evidence-based practice were the same. It was observed that a relatively low percentage (33.3%) of the participants had previously undergone formal leadership training, suggesting a significant potential for further growth and improvement in nurse managers. The results of this study underscore the critical need for leadership competency intervention programs in healthcare settings. A program aimed at enhancing head nurses' leadership competencies resulted in increased leadership competence and effectiveness. It is therefore recommended that such programs be developed and implemented to equip head nurses with the necessary leadership skills to fulfil their management roles effectively and to support them in becoming proficient nurse leaders. The urgency and importance of this recommendation cannot be overstated, and adequate resources must be allocated to establish and sustain such programs.
Nurse managers, leadership development, nursing management, leadership effectiveness
Burnout among Nurses in Intensive Care Unit in Saudi Arabia: Implications for Nursing Management
Burnout among ICU nurses in Saudi Arabia poses a serious challenge, impacting both staff well-being and patient care. The high-stress environment and demanding workload contribute significantly to this issue. This study aims to explore the prevalence and factors influencing burnout among ICU nurses in Saudi Arabia. A cross-sectional survey was conducted among 200 ICU nurses in Saudi Arabia. Participants completed the Maslach Burnout Inventory, which measures emotional exhaustion, depersonalization, and personal accomplishment. Data were collected over three months and analyzed using descriptive statistics to determine prevalence rates, as well as inferential statistics to identify factors influencing burnout. The analysis focused on demographic variables, work conditions, and their impact on burnout levels. The study found that 70% of ICU nurses experienced high levels of emotional exhaustion, 55% reported moderate depersonalization, and 60% felt a reduced sense of personal accomplishment. Emotional exhaustion was most prevalent among younger nurses with less experience and those working in high-stress environments. Depersonalization was linked to insufficient support and high patient turnover. Factors such as gender and work shifts also significantly impacted burnout levels, indicating a need for tailored interventions. Addressing burnout through targeted interventions, improved support systems, and better working conditions is essential to enhance nurse well-being and patient care quality in ICU settings.
Nurses, Burnout, Intensive Care Unit, Factors