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.

This article presents a bibliographic review on the evolution of Geographic Information Systems (GIS) and their integration in the social sciences, which is important because the interrelation of these areas contributes to the knowledge of the people. In this sense, the objective was to contribute to the university academic knowledge, through the compilation, classification, analysis and synthesis of scientific works according to the subject treated. For this purpose, the historical, synthetic, dialectical, and analytical methods were used, with a descriptive and documentary type of research, obtaining as a result that the GIS are very useful in different fields of social sciences, ranging from archeology to sociology, including specific topics such as economics and criminology.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.

Context: Noise in the work environment, in all types of productive activities, represents a hazard and has not really been valued in its real dimension. Little has been seen that stakeholders have determined the urgency of managing noise control programs. Therefore, losses resulting from medical treatment and absenteeism, represented in health care and social services, result in hidden work-related costs that directly affect the gross domestic product in any country.

Method: This article compiles different case studies from around the world. The studies were divided for review into general studies on the effects of workforce noise and then particularized according to the effects of industrial noise on workers’ health. At a control level, the assessment and measurement of noise is defined through the use of tools such as noise maps and their respective derivations, in addition to spatial databases.

Results: According to the collection of information and its analysis, we observe that in the medium term, the economies will be diminished in an important percentage due to the consequences generated by the exposure to noise. Specific information can be found in the development of the article.

Conclusions: The data provided by the case studies point to the need for Colombia, a country that is no stranger to this phenomenon, and which additionally has the great disadvantage of not having significant studies in the field of noise analysis, should strengthen studies based on spatial data as a mechanism for measurement and control.

Financing: Fundación universitaria Los Libertadores.

The design of effective flood risk mitigation strategies and their subsequent implementation is crucial for sustainable development in mountain areas. The assessment of the dynamic evolution of flood risk is the pillar of any subsequent planning process that is targeted at a reduction of the expected adverse consequences of the hazard impact. This study focuses on riverbed cities, aiming to analyze flood occurrences and their influencing factors. Through an extensive literature review, five key criteria commonly associated with flood events were identified: slope height, distance from rivers, topographic index, and runoff height. Utilizing the network analysis process within Super Decision software, these factors were weighted, and a final flood risk map was generated using the simple weighted sum method. 75% of the data was used for training, and 25% of it was used for testing. Additionally, vegetation changes were assessed using Landsat imagery from 2000 and 2022 and the normalized difference vegetation index (NDVI). The focus of this research is Qirokarzin city as a case study of riverbed cities, situated in Fars province, with Qir city serving as its central hub. Key rivers in Qirokarzin city include the Qara Aghaj River, traversing the plain from north to south; the primary Mubarak Abad River, originating from the east; and the Dutulghaz River, which enters the eastern part of the plain from the southwest of Qir, contributing to plain nourishment during flood events. The innovation of this paper is that along with the objective to produce a reliable delineation of hazard zones, a functional distinction between the loading and the response system (LS and RS, respectively) is made. Results indicate the topographic index as the most influential criterion, delineating Qirokarzin city into five flood risk zones: very low, low, moderate, high, and very high. Notably, a substantial portion of Qirokarzin city (1849.8 square kilometers, 8.54% of the area) falls within high- to very-high flood risk zones. Weighting analysis reveals that the topographic humidity index and runoff height are the most influential criteria, with weights of 0.27 and 0.229, respectively. Conversely, the height criterion carries the least weight at 0.122. Notably, 46.7% of the study area exhibits high flood intensity, potentially attributed to variations in elevation and runoff height. Flood potential findings show that the middle class covers 32.3%, indicating moderate flood risk due to changes in elevation and runoff height. The low-level risk is observed sporadically from the east to the west of the study area, comprising 12.4%. Analysis of vegetation changes revealed a significant decline in forest and pasture cover despite agricultural and horticultural development, exacerbating flood susceptibility. 

This study introduces a model designed to improve the strategic readiness of private hospitals in Amman by incorporating strategic competencies as an independent variable and using a healthcare information system as a mediator. Targeting private hospitals with over 140 beds, the research included a population of 3263 employees across various managerial levels. Data collection methods involved interviews and electronic questionnaires, resulting in a sample size of 344. Statistical analyses comprised exploratory and confirmatory factor analysis, structural equation modeling, and hypothesis testing with SMART PLS 3.3.3 software. The results indicated medium levels of both strategic competencies and healthcare information systems, while strategic readiness was found to be low. Nevertheless, the proposed model showed a direct positive effect of strategic competencies on strategic readiness, with the healthcare information system acting as a significant partial mediator. Evaluation metrics included the arithmetic mean, standard deviation, and path analysis. This model surpasses traditional methods by effectively linking strategic competencies and information systems to enhance strategic readiness, providing a strong framework for improving hospital responses to crises and dynamic changes. The study suggests focusing on enhancing and developing strategic competencies and integrating a comprehensive healthcare information system to optimize hospital operations and increase readiness.