Assessing the quality of nursing clinical placement: A quantitative cross-sectional study.

AIM: To evaluate the quality of nursing clinical placement among nursing students. DESIGN: This is a descriptive cross-sectional study. METHODS: Two hundred eighty two nursing student completed self-administered, online questionnaires. The questionnaire assessed participants' socio-demographic data, and the quality of their clinical placement. RESULTS: The students had a high mean score for the overall satisfaction of their clinical training placement with high mean score for the item of "patient safety was fundamental to the work of the units" and the item of "I anticipate being able to apply my learning from this placement," while the lowest mean score was related to "This placement was a good learning environment" and "Staff were willing to work with students." Patient or Public Contribution: Quality of clinical placement is critical for improving the everyday quality of care for patients who are in desperate need of caregivers with professional knowledge and skills.

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

Background: Needle 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. Methods: Three 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%. Results: The 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). Conclusion: Although 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.

The Association between Nursing Students' Happiness, Emotional Intelligence, and Perceived Caring Behavior in Riyadh City, Saudi Arabia.

BACKGROUND: Caring behavior is a major focus of the nursing profession and an important dimension of nursing practice that sets nurses apart from other healthcare professionals. Effective patient-centered care requires ensuring nurses have the emotional intelligence and happiness to address the daily demands of practice. The purpose of this study is to examine the emotional intelligence and happiness among nursing students and their relationship with caring behaviors. METHODS: A cross-sectional, descriptive correlational study was conducted on nursing students (n = 363) from Riyadh, Kingdom of Saudi Arabia, via an online survey. Measures include demographic data survey, Oxford Happiness Questionnaire, Trait Emotional Intelligence Questionnaire, and Caring Behaviors Inventory scale. Descriptive and multiple regression analyses were conducted for this study. RESULTS: Nursing students reported their highest degree of caring was in terms of 'respectful differences to others', while their lowest was in 'knowledge and skills'. Emotional intelligence and happiness were significant predictors of caring behaviors and explained the variance in assurance of human presence (17.5%), knowledge and skills (17.5%), respectful differences to others (18%), and positive connectedness (12.9%). In the final regression model, emotional intelligence and happiness were significant predictors of caring behaviors and explained 19.5% of the variance. CONCLUSIONS: Emotional intelligence and happiness among nursing students were found to be important factors to improve their caregiving behaviors. Therefore, nursing educators should consider integrating emotional intelligence and happiness interventions for students into their curriculum.

Organizational culture and readiness for evidence-based practice in the Kingdom of Saudi Arabia: A pre-experimental study.

AIM: This study aims to establish postgraduate students' perceptions of the organizational culture and readiness for evidence-based practice of their workplaces in the Kingdom of Saudi Arabia. BACKGROUND: Nurse shortages and a reliance on a transient nurse workforce have long been a challenge in the Kingdom of Saudi Arabia. Developing a home-grown nurse workforce, a key objective of the Government of Saudi Arabia, can help to address this. Evidence-based practice offers a mechanism to address this. Evidence-based practice implementation is heavily reliant on the prevailing organizational culture. Establishing the organizational culture and readiness for evidence-based practice is crucial for sustainable evidence-based practice implementation. METHODS: A pre-experimental pilot study collected data from the same participants at three different points. As part of this, a questionnaire measuring organizational culture and readiness for evidence-based practice was administered twice. Descriptive, inferential and correlational statistics were employed to analyse the data. RESULTS: Results demonstrated improved participant perceptions of the organizational culture and readiness for evidence-based practice of their workplaces between the first (M = 76.58, SD = 19.2) and second (M = 92.10, SD = 23.68) data collection points, indicating moderate movement towards a culture of evidence-based practice. Strengths, challenges and opportunities for improvement were identified. CONCLUSION: This study established participants' perceptions of the organizational culture and readiness for evidence-based practice of their workplaces, affording insight into context-specific strategies to embed evidence-based practice in health care organizations. IMPLICATIONS FOR NURSING MANAGEMENT: Assessing an organization's culture and readiness for evidence-based practice (EBP) can afford insight on the strengths, challenges and opportunities that exist to equip nurse managers to advance evidence-based practice at individual, professional and organizational levels. This study demonstrated the importance of promoting an environment conducive to EBP and putting in place the necessary resources to support evidence-based practice implementation. Nurse managers can play a central role in this.

An optimized deep learning approach for suicide detection through Arabic tweets.

Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.

A3C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer.

Alzheimer's disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer's disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer's patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A3C-TL-GTO framework for MRI image classification and AD detection. The A3C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer's Dataset (four classes of images) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer's disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer's Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.

Evaluating Nursing Staff Perception of Hospital Readiness for Continuity of Essential Health Care Services and Surge Capacity in Line With COVID-19.

OBJECTIVE: To evaluate nursing staff' perception of hospital readiness for continuity of essential health care services and surge capacity in line with COVID-19. METHODS: A total of 300 nurses were recruited from one hospital in Saudi Arabia. They completed self-administered, online questionnaires. The questionnaire assessed participants' socio-demographic data and their perceptions regarding hospital readiness for continuity of essential health care services and surge capacity in line with COVID-19. RESULTS: The findings revealed that nursing staff had a moderate mean score regarding hospital readiness for continuity of health care services (3.89 ± 0.61) and an average mean value regarding surge capacity of 3.83 ± 0.63. Also, the value of R2 of surge capacity in healthcare can predict 82.9% of the variance in hospital readiness for continuity of health care services in terms of surge capacity. CONCLUSION: Hospital administrators could propose hospital regulations and protocols for the management of confirmed and suspected COVID-19 patients in addition to designing a continuing education program for health professionals at all levels related to prevention, control, and management of COVID-19 suspected and confirmed patients.

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network.

Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R-CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes).

A Cross-sectional Study About Nurses' and Physicians' Experience of Disaster Management Preparedness Throughout COVID-19.

OBJECTIVE: The aim of this study was to assess and compare nurses' and physicians' knowledge of disaster management preparedness. An effective health-care system response to various disasters is paramount, and nurses and physicians must be prepared with appropriate competencies to be able to manage the disaster events. METHODS: This is a cross-sectional study. A total of 636 nurses and 257 physicians were recruited from 1 hospital in Saudi Arabia. Of them, 608 (95.6%) nurses and 228 (83.2%) physicians completed self-administered, online questionnaires. The questionnaire assessed participants' sociodemographic data, and disaster management knowledge. RESULTS: The findings revealed that participants had more knowledge regarding the disaster preparedness stage than mitigation and recovery stages. They also reported a need for advanced disaster training areas. A total of 10.1% of nurses' and 15.6% of physicians' overall knowledge is explained by their demographic and work-related characteristics. CONCLUSIONS: Both nurses and physicians had to some extent knowledge regarding the information and practices required for disaster management process. It is proposed that hospital managers must look for opportunities to effectively adopt national standards to manage disasters and include nurses and physicians in major-related learning activities because experience has suggested a somewhat low overall perceived competence in managing disaster situations.

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.

A pre-experimental pilot study exploring EBP Beliefs and EBP Implementation among post-graduate student nurses in Saudi Arabia.

AIM: This study aimed to measure the impact of a dedicated EBP module on the knowledge, skills and capability for EBP of students undertaking the inaugural MSc in Nursing: Advanced Practice programme in the KSA. BACKGROUND: Evidence-based practice (EBP) yields multiple benefits for all key stakeholders of healthcare. Key to this are healthcare professionals armed with necessary EBP knowledge and skills. Nurses, the largest professional group in healthcare, can be instrumental in effecting sustained EBP implementation. In the Kingdom of Saudi Arabia (KSA) achieving this is hindered by a chronic shortage of nurses and a heavy reliance on expatriate nurses who are often a transient workforce, resulting in a high turnover. The Government of Saudi Arabia 2030 Vision aspires to address the indigenous nurse shortage and the quality of healthcare. In 2017 the inaugural MSc in Nursing: Advanced Practice programme was established in the KSA to prepare Saudi nurses for emerging advanced practice roles. A dedicated EBP module was a core component of the programme. METHODS: A pre-experimental pilot study conducted over 18-months collected data from the same participants at three different points. Two validated EBP questionnaires measuring EBP Beliefs and EBP Implementation were administered to post-graduate students undertaking the MSc in Nursing: Advanced Practice programme in one Higher Education Institution in the KSA. Descriptive, inferential and correlational statistics were employed to analyse the demographic data, group mean scores and distribution on the EBP scales, as well the correlation between EBP Beliefs and EBP Implementation. FINDINGS: Findings demonstrated that the educational intervention did improve participants' EBP beliefs and implementation. Participants reported positive beliefs about EBP at all 3 data collection points (M = 57.4 SD = 7.0; M = 62.54 SD = 7.21; M = 55.31 SD = 15.81, respectively). EBP implementation was low prior to undertaking the module but improved thereafter as illustrated across the 3 data collection points (M = 15.14 SD = 11.9; M = 27.64 SD = 14.35; M = 25.9 SD = 20.43). On both measures, higher scores indicate higher EBP beliefs and implementation. CONCLUSION: This study established the EBP Beliefs and EBP Implementation of a sample of postgraduate nursing students in the KSA. Findings revealed a substantial improvement in both EBP Beliefs and EBP Implementation following the EBP module. Findings support the use of a dedicated module to prepare nurses to use EBP and to practice at an advanced level while simultaneously preparing them for leadership roles in healthcare in KSA. In so doing, this will help to advance the healthcare goals of the KSA 2030 vision.

The relationship between nurses' job crafting behaviours and their work engagement.

AIM: To investigate the relationship between job crafting and work engagement among hospital nurses. BACKGROUND: Job crafting is a relatively advanced job redesign concept, and few studies have investigated it among nurses. METHODS: This is a cross-sectional study. A total of 636 nurses were recruited from one hospital in Saudi Arabia. Of them, 608 (95.6%) completed self-administered, online questionnaires. The questionnaire assessed participants' socio-demographic data, job crafting and work engagement. Structured equation modelling (SEM) was used to examine the association between job crafting and work engagement. RESULTS: Data from 549 nurses were analysed. Most of the participants (85.1%) were females, and their mean scores of job crafting and work engagement were 3.54 ± 0.5 and 4.77 ± 1.1, respectively. The SEM revealed that job crafting accounted for 57% of the variance of work engagement. CONCLUSIONS: Job crafting is a significant determinant of nurses' work engagement. IMPLICATIONS FOR NURSING MANAGEMENT: Supporting staff nurses to employ job crafting behaviours would positively improve their work engagement. This may include, but is not limited to, helping nurses to bargain a significance in their labour, reforming the work pattern in a manner that lines up with organisational objectives and employing an innovative managerial style.