Patient safety concept has achieved more attention from healthcare organizations to improve the safety culture. This study aimed to investigate patient safety attitudes among doctors and nurses and explore associations between workload, adverse events, and experience with patient safety attitudes. The study used a descriptive cross-sectional design and the Turkish version of the Safety Attitudes Questionnaire. Participants included 73 doctors and 246 nurses working in two private hospitals in Northern Cyprus. The participants had negative perceptions in all patient safety domains. The work conditions domain received the highest positive perception rate, and the safety climate domain received the lowest perception rate among the participants. Nurses showed a higher positive perception than doctors regarding job satisfaction, stress recognition, and perceptions of management domains. There were statistically significant differences between experiences, workloads, adverse events, and total mean scores of patient safety attitudes. Policymakers and directors can improve the quality of care of patients and patient safety by boosting the decision-making of health care providers on several domains of safety attitudes. Patient safety needs to be improved in hospitals through in-service education, management support, and institutional regulations.

This research study examines academic self-perceptions and course satisfaction among university students and associated factors during virtual classes. A cross-sectional online survey of (n = 328) undergraduate and postgraduate Saudi students who took virtual classes during the second semester of the academic year 2019–2020 and the first semester of the academic year 2020–2021 during the coronavirus disease 2019 (COVID-19) pandemic. The findings demonstrated students’ scores on negative academic self-perceptions (mean (M) = 9.84; standard deviation (S.D.) = 3.09) are significantly higher in comparison to positive academic self-perceptions (M = 7.71; S.D. = 2.46) and the difference was statistically significant, t(327) = 3.69, p < 0.001. The analysis demonstrated that mean differences were significant across ‘year of study’, ‘field of study’, ‘CGPA’ (cumulative grade points average), ‘employment status’, ‘on-site work’ and ‘being a parent of young child’ (p < 0.01). Correlation analysis shows a linear positive association between perceptions of workload and low technical support with negative academic self-perceptions (p < 0.001) and an inverse relationship with positive academic self-perceptions (p < 0.001). The multiple regression analysis demonstrated that the predictor variables in the model (perceptions of workload and technical support) explain 62% variance in negative academic self-perceptions and 41% variance in positive academic self-perceptions. Furthermore, the analysis demonstrated that positive academic self-perceptions bring a 32% variance in course satisfaction. These findings underscore the importance of balancing workload during online studies in higher education and provision of adequate technical support to reduce the negative academic self-perceptions which are associated with lower levels of course satisfaction. Students’ academic self-perceptions and course satisfaction during virtual studies are important factors to retain students’ motivation in learning and academic performance.

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

This study aims to explore the potential mediation role of person-centeredness between the effects of the work environment and nurse reported quality and patient safety. A quantitative cross-sectional survey collected data from 1055 nurses, working in medical and surgical units, in twelve Malaysian private hospitals. The data collection used structured questionnaires. The Hayes macro explored the mediation effect of person-centeredness between the associations of work environment dimensions and care outcomes, controlling nurses’ demographics and practice characteristics. A total of 652 nurses responded completely to the survey (61.8% response rate). About 47.7% of nurses worked 7-h shifts, and 37.0% were assigned more than 15 patients. Higher workload was associated with unfavorable outcomes. Nurses working in 12-h shifts reported a lower work environment rating (3.46 ± 0.41, p < 0.01) and person-centered care (3.55 ± 0.35, p < 0.01). Nurses assigned to more than 15 patients were less likely to report a favorable practice environment (3.53 ± 0.41, p < 0.05), perceived lower person-centered care (3.61 ± 0.36, p < 0.01), and rated lower patient safety (3.54 ± 0.62, p < 0.05). Person-centeredness mediates the effect of nurse work environment dimensions on quality and patient safety. Medical and surgical nurses, working in a healthy environment, had a high level of person-centeredness, which, in turn, positively affected the reported outcomes. The function of person-centeredness was to complement the effects of the nurse work environment on care outcomes. Improving the nurse work environment (task-oriented) with a high level of person-centeredness (patient-oriented) was a mechanism through which future initiatives could improve nursing care and prevent patient harm.

The tobacco use epidemic is one of the major global public health challenges and causes > 7 million deaths each year, including ~70 000 Saudis who die from smoking-related diseases. To present recent government initiatives in Saudi Arabia that have been designed to combat tobacco use in the country. This was a review based on secondary data sources such as published reports, articles in newspapers, and research studies published in various journals. We present initiatives taken from June 2017 to April 2019 by the Saudi government to combat tobacco use, including value-added tax on tobacco, antismoking campaigns, antismoking clinics, mobile apps and other initiatives. The study suggests that the government should evaluate the impact of these initiatives on tobacco control in Saudi Arabia.

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease’s spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.