In today’s rapidly evolving organizational landscape, understanding the dynamics of employee incentives is crucial for fostering high performance. This research delves into the intricate interplay between moral and financial incentives and their repercussions on employee performance within the dynamic context of healthcare organizations. Drawing upon a comprehensive analysis of 226 respondents from three healthcare organizations in Klang Valley, Peninsular Malaysia, the study employs a quantitative approach to explore the relationships between independent variables (career growth, recognition, decision-making, salary, bonus, promotion) and the dependent variable of employee performance. The research unveils that moral incentives, including career growth, recognition, and decision-making, significantly impact employee performance. Professionals motivated by opportunities for growth, acknowledgment, and participation in decision-making demonstrate heightened engagement and commitment. In the financial realm, competitive salaries, performance-based bonuses, and transparent promotion pathways are identified as crucial factors influencing employee performance. The study advocates a holistic approach, emphasizing the synergistic integration of both moral and financial incentives. Healthcare organizations are encouraged to tailor their incentive structures to create a supportive and rewarding workplace, addressing the multifaceted needs and motivations of healthcare professionals. The implications extend beyond academia, offering practical guidance for organizations seeking to optimize workforce dynamics, foster job satisfaction, and ensure the sustainability of healthcare organizations.

This article analyses the complex factors contributing to rising medical expenses, focusing on the senior citizen demographic in Malaysia. With the global aging population, notably in lower and middle-income countries, the study highlights the escalating medical and health insurance costs, driven by age, income source, modern healthcare, and geographical residence. The research draws on an extensive literature review, demographic analysis, and quantitative methods to examine these determinants. It critically analyzes Malaysia’s healthcare system, which operates on a dual-tier model, and the financial burden placed on senior citizens. The findings indicate that age, source of income, and geographical residence significantly influence medical expenses, whereas modern healthcare’s impact is not statistically significant. The study calls for government intervention, insurance industry adjustments, and private sector support to mitigate the financial strain on senior citizens. Recommendations include tax relief adjustments, National Health Insurance Scheme implementation, and employment sustainability for seniors. This research provides some recommendations to policymaking, the insurance industry, and academia by providing insights into managing the healthcare needs of an aging population sustainably.

An exhaustive analysis and evaluation of fertility indicators in a society including many ethnic groups might provide valuable insights into any discrepancies. This study aims to systematically analyse the fertility rates over specific periods and investigate the differences in levels and patterns between local and expatriate women in Saudi Arabia using the existing data. This analysis used data from credible sources published by the General Authority for Statistics in the Saudi census 2022. The calculation of period fertility indicators started with the most straightforward rates and advanced to more complex ones, followed by a comprehensive description of the advantages and disadvantages of each. The aim was to ascertain fluctuations in fertility rates and analyse temporal patterns. Multiple studies consistently show that the fertility rate among expats in Saudi Arabia is lower than that among Saudi native women. However, the reason for this discrepancy still needs to be discovered since the definitive effect of contraceptive techniques has yet to be confirmed. Moreover, the reproductive trends that have occurred since the early 1980s will persist, although with additional precautions in place.

Introduction: In Central Europe, in Hungary, the state guarantees access to health care and basic health services partly through the Semmelweis Plan adopted in 2011. The primary objectives of the Semmelweis Plan include the optimisation and transformation of the health care system, starting with the integration of hospitals and the state control of previously municipally owned hospitals. The transformation of the health care system can have an impact on health services and thus on meeting the needs of the population. In addition to reducing health inequalities and costs, the relevant benefits include improving patients’ chances of recovery and increasing patient safety. The speciality under study is decubitus care. Our hypothesis is that integration will improve the chances of recovery for decubitus patients through access to smart dressings to promote patient safety. Objective: to investigate and demonstrate the effectiveness of integration in improving the chances of recovery for decubitus ulcer patients. Material and methods: The research compared two time periods in the municipality of Kalocsa, Bács-Kiskun County, Southern Hungary. We collected the number of decubitus patients arriving and leaving the hospital from the nursing records and compared the pre-integration period when decubitus patients were provided with conventional dressings (01.01.2006–2012.12.31) and the post-integration period, which entailed the introduction of smart dressings in decubitus care (01.01.2013–2012.12.31). The target population of the study was men and women aged 0–99 years who had developed some degree of decubitus. The sample size of the study was 4456. Independent samples t-test, Chow test and linear trend statistics were used to evaluate the results. Based on the empirical evidence, a SWOT analysis was conducted to further examine the effectiveness of integration. Results: The independent samples t-test model used was significant (for Phase I: t (166) = −16.872, p < 0.001; for Phase II: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −16.872, p < 0.001). For stage III: t (166) = −10.078, p < 0.001; for stage IV: t (166) = −10.078, p < 0.001; for stage III: t (166) = −10.078, p < 0.001). for stage III: t (166) = −14.066, p < 0.001). For the Chow test, the p-values were highly significant, indicating a structural break. Although the explanatory power of the regression models was variable (R-squared values ranged from 0.007 to 0.617), they generally supported the change in patient dynamics after integration. Both statistical analyses and SWOT analysis supported our hypothesis and showed that integration through access to smart dressings improves patients’ chances of recovery. Conclusions: Although only one segment of the evidence on the effectiveness of hospital integration was examined in this study, integration in the study area had a positive impact on the effective care of patients with decubitus ulcers, reduced inequalities in care and supported patient safety. In the context of the results obtained, these trends may reflect different systemic changes in patient management strategies in addition to efficient allocation of resources and quality of care.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

The rapid advancement of information and communication technology has greatly facilitated access to information across various sectors, including healthcare services. This digital transformation demands enhanced knowledge and skills among healthcare providers, particularly in comprehensive midwifery care. However, midwives in rural areas face numerous challenges such as limited resources, cultural factors, knowledge disparities, geographic conditions, and technological adoption. This research aims to evaluate the impact of AI utilization on midwives’ knowledge and behavior to optimize the implementation of healthcare services in accordance with Delima Midwife Service standards in rural settings. The analysis encompasses competencies, characteristics, information systems, learning processes, and health examinations conducted by midwives in adopting AI. The research methodology employs a cross-sectional approach involving 413 rural midwives selected proportionally. Results from Partial Least Squares Structural Equation Modeling indicate that all reflective evaluation variables meet the required criteria. Fornell-Larcker criterion demonstrates that the square root of AVE is greater than other variables. The primary findings reveal that information systems (0.029) and midwives’ competencies (0.033) significantly influence AI utilization. Furthermore, midwives’ competencies (0.002), characteristics (0.031), and AI utilization (0.011) also significantly impact midwives’ knowledge and behavior. Midwives’ characteristics also significantly affect their competencies (0.000), while midwives’ learning influences health examinations (0.000). Midwives’ knowledge and behavior affect the transformation of healthcare services in rural midwifery (0.022). The model fit results in a value of 0.097, empirically supporting the explanation of relationships among variables in the model and meeting the established linearity test.