Good health and well-being are embedded in the 3rd Goal amongst the UN Sustainable Development Goals. The primary objective of this research was to identify the most critical economic, social, and administrative barriers to implementing the Expanded Program on Immunization (EPI) in the Punjab Province of Pakistan. A sequential exploratory design and case study technique were used, employing both qualitative and quantitative methods. In the first stage, in-depth interviews with 50 key officials were conducted to identify the most critical barriers to the EPI program. A quantitative analysis was then performed based on the results obtained from qualitative analysis, and rank orders of barriers were received from the same health department experts. The results indicate that twenty-eight barriers can cause implementation problems for this program. Still, the ten barriers that gained the maximum hits are the most important barriers, which include Shortage of vaccinators, mismanagement of vaccines’ cold chain, biometric android application, ice-lined refrigerators, communication gap, inadequate legislation of EPI program, capacity building issues with EPI staff, Misconceptions about EPI program, lack of awareness of the parents and community, refusal cases and inadequate cooperation of lady health workers (LHWs). Coordinated efforts of the government and the public are highly recommended to address these barriers.

Diabetic retinopathy (DR) is a major cause of blindness globally. Effective screening programs are essential to mitigate this burden. This review outlines key principles and practices in implementing DR screening programs, emphasizing the roles of technology, patient education, and healthcare system integration. Our analysis highlights key principles for establishing successful screening initiatives, including the importance of regular screenings, optimal intervals, recommended technologies, and necessary infrastructure. We emphasize the roles of healthcare providers, patients, and policymakers in ensuring the effectiveness of these programs. Our recommendations aim to support the creation of robust policies that mitigate the impact of DR, ultimately improving public health outcomes and reducing the incidence of blindness due to diabetic retinopathy.

The aim of this study was to make a quantitative contribution to the impact of COVID-19 and Mental on consumer behavior. For this purpose, the data in the Scopus and WoS databases until 5 February 2024 were examined using bibliometric analysis. The data obtained within the scope of this study were classified and analyzed using the VOSviewer program developed for scientific mapping analysis. In the evaluations, 180 studies in the Web of Science database and 371 documents in the Scopus database were identified, and when duplicate studies were combined, 426 studies were included in the analysis. According to the results of the analysis, the journal with the highest number of publications is “Journal of Retailing and Consumer Services”; the organization with the highest number of publications is “Department of management sciences, University of Okara” and “North-West University”; the authors with the highest number of publications and citations are “Wang, Xueqin” and “Yuen, Kum Fai”; and the most cited studies are “Laato et al.” and “Goolsbee and Syverson”. This study provides a comprehensive analysis of the studies on the impact of COVID-19 and mental factors on consumer behavior and makes a qualified contribution to the literature with an important opening.

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.

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.