Advanced Statistical Methods Research Articles

Research on the Influencing Mechanism of Digital Transformation on the Synergistic Effect and Carbon Emission Performance of Enterprise Green Supply Chain

This paper studies the impact mechanism of digital transformation on the synergy effect and carbon emission performance of enterprises’ green supply chain, and reveals the key role of digital technology in improving enterprises’ environmental efficiency and reducing carbon emission through in-depth discussion of relevant theories and empirical analysis. Firstly, the paper summarizes the connotation of digital transformation and its application trend in modern enterprise operations, and expounds the importance of green supply chain synergies and the emission reduction needs of enterprises in pursuit of carbon neutrality.Through literature review and theoretical construction, this paper proposes a conceptual model that details how digital transformation can enhance the synergy of enterprises’ green supply chain by optimizing information transparency, improving decision-making efficiency, promoting resource conservation and technological innovation. Further, the model reveals how this synergistic effect can indirectly or directly lead to improved carbon performance by improving production processes, logistics management, and product lifecycle management.The empirical research part, based on rich sample data and advanced statistical methods, verifies the positive correlation between digital transformation and green supply chain synergies and the negative correlation between digital transformation and carbon emission performance. At the same time, structural equation model and case analysis are used to deeply analyze the concrete manifestation and implementation effect of these influencing mechanisms.Finally, based on the research findings, this paper puts forward specific suggestions for enterprises and policy makers, emphasizes the strategic significance of promoting digital transformation to enhance green supply chain synergies and optimize carbon emission performance, and also points out the issues that need to be paid attention to and the development direction of future research. This study provides important theoretical support and practical guidance for understanding and practicing how enterprises use digital means to promote green development.

Drug Repurposing in Crohn's Disease Using Danish Real-World Data.

Drug repurposing, utilizing electronic healthcare records (EHRs), offers a promising alternative by repurposing existing drugs for new therapeutic indications, especially for patients lacking effective therapies. Intestinal fibrosis, a severe complication of Crohn's disease (CD), poses significant challenges, increasing morbidity and mortality without available pharmacological treatments. This article focuses on identifying medications associated with an elevated or reduced risk of fibrosis in CD patients through a population-wide real-world data and artificial intelligence (AI) approach. Patients aged 65 or older with a diagnosis of CD from 1996 to 2019 in the Danish EHRs were followed for up to 24 years. The primary outcome was the need of specific surgical procedures, namely proctocolectomy with ileostomy and ileocecal resection as proxies of intestinal fibrosis. The study explored drugs linked to an increased or reduced risk of the study outcome through machine-learning driven survival analysis. Among the 9179 CD patients, 1029 (11.2%) underwent surgery, primarily men (58.5%), with a mean age of 76 years, 10 drugs were linked to an elevated risk of surgery for proctocolectomy with ileostomy and ileocecal resection. In contrast, 10 drugs were associated with a reduced risk of undergoing surgery for these conditions. This study focuses on repurposing existing drugs to prevent surgery related to intestinal fibrosis in CD patients, using Danish EHRs and advanced statistical methods. The findings offer valuable insights into potential treatments for this condition, addressing a critical unmet medical need. Further research and clinical trials are warranted to validate the effectiveness of these repurposed drugs in preventing surgery related to intestinal fibrosis in CD patients.

The role of employee engagement towards innovative work behavior mediated by leadership in small businesses

This study aims to examine how employee involvement, leadership, and innovation-related actions are linked in small businesses in Saudi Arabia. As Saudi Arabia works on diversifying its economy and considers innovation essential for growth, this research seeks to identify key factors and processes that promote innovation in companies. A survey was conducted across different small businesses in Saudi Arabia to gather information from employees. Advanced statistical methods, like structural equation modeling (SEM), were used to analyze the data and understand the relationships being studied. The results show a strong, positive link between how engaged employees are and their innovation-related actions in Saudi Arabian small businesses. The study also reveals how crucial leadership is in this process, demonstrating how leaders can either encourage or hinder employees' ability to contribute to innovation. These findings are useful for small business owners and leaders in Saudi Arabia, guiding them on how to foster an environment that supports innovation and improves their competitive edge in the global market. This research is also valuable for policymakers, business leaders, and academics interested in promoting innovation and long-term growth in this vital part of Saudi Arabia's economy.

Statistical Analysis of Radioactivity: Lamprecht Uranium Mine in Texas.

The former Lamprecht uranium mine facility in Texas ceased operations well before the new millennium. However, decommissioning activities were never completed by the licensee. Consequently, a legal proceeding was authorized between state and licensee representatives. Meanwhile, state funds were used to hire an independent contractor to perform radiological surveys and assess the magnitude of residual radioactivity across the terrain at the site. The purpose of this study was to apply advanced spatial statistical methods to the survey data measured by contractors at the Lamprecht site to precisely predict remaining radioactive hotspot locations post soil remediation activities. To accomplish this, descriptive statistics such as Google maps and boxplots along with inferential spatial statistical techniques, e.g., kriging and semivariograms, were employed. R coding was also used throughout. Specifically, the descriptive statistical methods included geographical mapping of targeted areas at the site coupled with summary statistics. Inferentially, spatial analytical techniques were employed to pinpoint the locations of elevated radiation levels above regulatory limits. Our results suggest that fewer hotspots were identified after remediation activities were completed at the site. This study provides an additional analytical resource for the State of Texas regarding the release of this former in situ leach uranium mine site to landowners for unrestricted use.

The Importance of Educational Data Mining and Learning Analytics for Improving Teaching and Learning: An Issue Brief

“The words educational data mining and learning analytics are frequently used interchangeably, despite their being an increase in their investigation and implementation. This may be as a result of the fact that both areas have similar conceptual components. One way to ensure precision, homogeneity, and consistency It aims to pinpoint themes that are similar to and different from one other in the two domains as they develop. This a topic modelling study of papers on educational data mining and learning analytics was carried out in the elucidate the two areas' respective themes. In particular, we used structural topic modelling to find the two domains' subjects from the abstracts. For instructional purposes, we use structural topic modelling on N 1 4192 articles. For both educational data and survey data, we infer five-topic models analytics for mining and learning. While there may be disciplinary variations in research, our findings show that beyond their various lineages, there is no evidence to indicate a clear separation between the two disciplines. the area of educational research on the uses of advanced statistical methods is trending toward convergence for improving teaching and learning, discover how to mine massive data streams for insights that may be put to use. Over the past five years, both areas have converged on a growing emphasis on student behaviour. This study topic has advanced greatly, and a variety of related words, including Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science, are now used in the paper. The main publications, significant turning points, cycle of knowledge discovery, primary educational settings, specialised tools, freely accessible datasets, widely used methodologies, primary goals, and anticipated trends in this field of study are reviewed to provide the state of the art at this time.

Dietary factors and hypertension: A Mendelian randomization analysis.

This research explores the causal link between dietary habits and hypertension through Mendelian randomization, providing distinct perspectives on the role of diet in addressing this worldwide health issue. Utilizing instrumental variables, we applied advanced statistical methods, including the weighted median, inverse variance weighted, and MR-Egger, to evaluate the impact of 17 dietary elements on hypertension. These elements ranged across various food groups, such as fruits, meats, vegetables, and beverages, both alcoholic and nonalcoholic. Our results identified a significant positive association of hypertension with weekly alcohol consumption (OR 1.340 [95%CI 1.0001 to 1.794], p = .0499) and poultry intake (OR 2.569 [95%CI 1.305 to 5.057], p = .00631). Conversely, a negative association was observed with lamb/mutton (OR 0.550 [95%CI 0.343 to 0.881], p = .0129), cheese (OR 0.650 [95%CI 0.519 to 0.813], p = .000159), tea (OR 0.797 [95%CI 0.640 to 0.993], p = .0433), cereal (OR 0.684 [95%CI 0.494 to 0.948], p = .0227), and dried fruit consumption (OR 0.492 [95%CI 0.343 to 0.707], p = .000127). These findings suggest that dietary modifications, such as increasing consumption of specific foods like cheese, lamb/mutton, tea, cereals, and dried fruits, could potentially reduce hypertension risk while reducing intake of alcoholic beverages and poultry might mitigate its increase. No direct causal relationships were established between other dietary factors and hypertension. The study highlights the importance of specific dietary modifications for the prevention and control of hypertension, making a substantial contribution to public health tactics and recommendations.

Daily activity profiles over the lifespan of female medflies as biomarkers of aging and longevity.

The relationship between the early-age activity of Mediterranean fruit flies (medflies) or other fruit flies and their lifespan has not been much studied, in contrast to the connections between lifespan and diet, sexual signaling, and reproduction. The objective of this study is to assess intra-day and day-to-day activity profiles of female Mediterranean fruit flies and their role as biomarker of longevity as well as to explore the relationships between these activity profiles, diet, and age-at-death throughout the lifespan. We use advanced statistical methods from functional data analysis (FDA). Three distinct patterns of activity variations in early-age activity profiles can be distinguished. A low-caloric diet is associated with a delayed activity peak, while a high-caloric diet is linked with an earlier activity peak. We find that age-at-death of individual medflies is connected to their activity profiles in early life. An increased risk of mortality is associated with increased activity in early age, as well as with a higher contrast between daytime and nighttime activity. Conversely, medflies are more likely to have a longer lifespan when they are fed a medium-caloric diet and when their daily activity is more evenly distributed across the early-age span and between daytime and nighttime. The before-death activity profile of medflies displays two characteristic before-death patterns, where one pattern is characterized by slowly declining daily activity and the other by a sudden decline in activity that is followed by death.

Statistical modeling of cargo securing on selected military trucks and road surfaces

The development of new technologies (e.g., three-axial accelerometers) and their implementation to the armed forces made it possible to rather easily obtain data on transportation with regard to cargo securing. It is, however, crucial to evaluate the data, and identify the factors important for the selection of an appropriate cargo securing system. Particular attention should be paid to the transportation on low-quality roads, or possibly in the terrain as it can be expected that the cargo (as well as the vehicle and the driver) is subjected to greater shocks. Data obtained from the military transport experiment have been processed using advanced statistical methods (a contaminated probability distribution, methods of statistical comparison, and an analysis of variance). The transportation data from the use of two military trucks on three different road surfaces has been compared. The results show, in which axes there are statistically significant differences in terms of the frequency of exceeding normatively determined acceleration coefficient values in relation to the road surface type and the vehicle type. Further results are based on the modeling of acceleration coefficients using a contaminated log-normal distribution, where the distribution of acceleration coefficient values is contaminated by outliers resulting from sudden changes in the transport conditions.

Galaxy Quenching at the High Redshift Frontier: A Fundamental Test of Cosmological Models in the Early Universe with JWST-CEERS

We present an analysis of the quenching of star formation in massive galaxies (M * > 109.5 M ⊙) within the first 0.5–3 Gyr of the Universe’s history utilizing JWST-CEERS data. We utilize a combination of advanced statistical methods to accurately constrain the intrinsic dependence of quenching in a multidimensional and intercorrelated parameter space. Specifically, we apply random forest classification, area statistics, and a partial correlation analysis to the JWST-CEERS data. First, we identify the key testable predictions from two state-of-the-art cosmological simulations (IllustrisTNG and EAGLE). Both simulations predict that quenching should be regulated by supermassive black hole mass in the early Universe. Furthermore, both simulations identify the stellar potential (ϕ *) as the optimal proxy for black hole mass in photometric data. In photometric observations, where we have no direct constraints on black hole masses, we find that the stellar potential is the most predictive parameter of massive galaxy quenching at all epochs from z = 0–8, exactly as predicted by simulations for this sample. The stellar potential outperforms stellar mass, galaxy size, galaxy density, and Sérsic index as a predictor of quiescence at all epochs probed in JWST-CEERS. Collectively, these results strongly imply a stable quenching mechanism operating throughout cosmic history, which is closely connected to the central gravitational potential in galaxies. This connection is explained in cosmological models via massive black holes forming and growing in deep potential wells, and subsequently quenching galaxies through a mix of ejective and preventative active galactic nucleus feedback.

Can a general factor be derived from employees’ responses to items on the Individual Work Performance Review?

This study aimed to investigate whether permissible inferences can be derived from employees’ standing on a general performance factor from their responses to the Individual Work Performance Review (IWPR) items. The performance of 448 employees was rated (by their managers) using the IWPR. Latent variable modelling was performed through a bifactor exploratory structural equation model with the robust version of the maximum likelihood estimator. The general factor’s score was also used to inspect correlations with two work performance correlates: tenure and job level. In line with international findings, the results suggested that a general factor could explain 65% of the common variance in the 80 items of the IWPR. Job level, but not tenure, correlated with general job performance. The results support calculating an overall score for performance, which might be a suitable criterion to differentiate top performers, conduct criterion validity studies, and calculate the return on investment of selection procedures or training programmes.Contribution: The present study provides initial evidence for a general factor influencing employees’ responses to items on a generic performance measure in South Africa. In addition, the study showcases the application of advanced statistical methods in factor analyses, demonstrating their efficacy in evaluating the psychometric properties of hierarchical factor models derived from data provided on performance measures.

ADVANCEMENTS IN GENOMIC TECHNOLOGIES AND THEIR IMPACT ON CROP IMPROVEMENT AND BREEDING METHODS

Recent advances in genome sequencing of essential crop plants allow genotype and phenotype data integration in crop development. Advanced statistical methods identified quantitative trait genes. Genomic prediction has been used successfully in breeding animals and is now also used in breeding plants. Biometric statistics, genome-wide markers, and phenotyping enhance gene discovery. This makes biotechnology agricultural plant solutions possible. Improved fertilizer efficiency requires precise plant phenotyping in multiple habitats and seasons, which was previously expensive. DNA sequencing, genetic engineering, and PCR-based marker-assisted selection have made it cheaper. New methods like next-generation sequencing can target climate-responsive crop development. It examines Oryza sativa L. flower opening and closing molecularly and hybrid breeding success in diverse plant types. It discusses CRISPR/Cas9 for crop trait improvement and metabolic studies for Triticum aestivum L. quality group determination. Genetic analysis should use species-specific techniques, according to the study. A Zea mays L. callus induction and growth study examined how media and growth conditions affect callus development. Another drought-stressed Triticum aestivum L. cultivar gene expression study suggests employing RNA editing to respond to environmental stress. PCR-based markers have helped scientists find important genes in landraces that have changed to survive harsh farming conditions, giving them targets for crop growth.

Teaching advanced statistical methods to postgraduate novices: a case example

Higher degree research students in education are largely underprepared for understanding or employing statistical data analysis methods. This is despite their need to read literature in their field which will indubitably include such research. This weakness may result in students choosing to use qualitative or interpretivist methodologies, even though education data are highly complex requiring sophisticated analysis techniques to properly evaluate the impact of nested data, multi-collinear factors, missing data, and changes over time. This paper describes a research methods course at a research-intensive university designed for students in a thesis-only degree program. The course emphasizes the logic and conceptual function of statistical methods and exposes students to hands-on tutorials in which students are required to conduct analyses with open-access data. The first half of the 12-week course focuses on core knowledge, normally taught in first-year probability and statistics courses. The second half focuses on introducing and modeling advanced statistical methods needed to handle complex problems and data. The course outline is provided along with descriptions of teaching and assessments. This exemplar functions as a potential model of how relative novices in statistical methods can be introduced to a conceptual use of statistical methods to raise the credibility of research.

Genetics and Genomics of Infectious Diseases in Key Aquaculture Species.

Diseases pose a significant and pressing concern for the sustainable development of the aquaculture sector, particularly as their impact continues to grow due to climatic shifts such as rising water temperatures. While various approaches, ranging from biosecurity measures to vaccines, have been devised to combat infectious diseases, their efficacy is disease and species specific and contingent upon a multitude of factors. The fields of genetics and genomics offer effective tools to control and prevent disease outbreaks in aquatic animal species. In this study, we present the key findings from our recent research, focusing on the genetic resistance to three specific diseases: White Spot Syndrome Virus (WSSV) in white shrimp, Bacterial Necrotic Pancreatitis (BNP) in striped catfish, and skin fluke (a parasitic ailment) in yellowtail kingfish. Our investigations reveal that all three species possess substantial heritable genetic components for disease-resistant traits, indicating their potential responsiveness to artificial selection in genetic improvement programs tailored to combat these diseases. Also, we observed a high genetic association between disease traits and survival rates. Through selective breeding aimed at enhancing resistance to these pathogens, we achieved substantial genetic gains, averaging 10% per generation. These selection programs also contributed positively to the overall production performance and productivity of these species. Although the effects of selection on immunological traits or immune responses were not significant in white shrimp, they yielded favorable results in striped catfish. Furthermore, our genomic analyses, including shallow genome sequencing of pedigreed populations, enriched our understanding of the genomic architecture underlying disease resistance traits. These traits are primarily governed by a polygenic nature, with numerous genes or genetic variants, each with small effects. Leveraging a range of advanced statistical methods, from mixed models to machine and deep learning, we developed prediction models that demonstrated moderate-to-high levels of accuracy in forecasting these disease-related traits. In addition to genomics, our RNA-seq experiments identified several genes that undergo upregulation in response to infection or viral loads within the populations. Preliminary microbiome data, while offering limited predictive accuracy for disease traits in one of our studied species, underscore the potential for combining such data with genome sequence information to enhance predictive power for disease traits in our populations. Lastly, this paper briefly discusses the roles of precision agriculture systems and AI algorithms and outlines the path for future research to expedite the development of disease-resistant genetic lines tailored to our target species. In conclusion, our study underscores the critical role of genetics and genomics in fortifying the aquaculture sector against the threats posed by diseases, paving the way for more sustainable and resilient aquaculture development.

Clinician's Approach to Advanced Statistical Methods: Win Ratios, Restricted Mean Survival Time, Responder Analyses, and Standardized Mean Differences.

Novel statistical methods have emerged in recent medical literature, which clinicians must understand to properly appraise and integrate evidence into their practice. Some of these key concepts include win ratios, restricted mean survival time, responder analyses, and standardized mean difference. This article offers guidance to busy clinicians on the comprehension and practical applicability of the results to patients. Win ratios provide an alternative method to analyze composite outcomes by prioritizing individual components of the composite; prioritization of the outcomes should be evidence-based, pre-specified, and patient-centered. Restricted mean survival time presents a method to analyze Kaplan-Meier curves when assumptions required for Cox proportional hazards analysis are not met. As it only considers outcomes that occur within a specific timeframe, the duration of follow-up must be appropriately defined and based on prior epidemiologic and mechanistic evidence. Researchers can analyze continuous outcomes with responder analyses, in which participants are dichotomized into "responders" or "non-responders." While clinicians and patients may more easily grasp outcomes analyzed in this way, they should be aware of the loss of information and resulting imprecision, as well as potential to manipulate data presentation. When meta-analyzing continuous outcomes, point estimates can be converted to standardized mean differences to facilitate the combination of data utilizing various outcome measures. However, clinicians may find it challenging to grasp the clinical meaningfulness of a standardized mean difference, and may benefit from converting it to well-known outcomes. By providing the background knowledge of these statistical methods, along with practical applicability, benefits, and inevitable limitations, this article aims to provide clinicians with an approach to appraise the literature and apply the results in clinical practice.

Demonstrative Evidence and the Use of Algorithms in Jury Trials

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom – where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.

A Study on Role Orientation and Professional Development of Early Childhood Teachers in Preschool Education in the Context of Big Data

Abstract This research delves into how early childhood teachers’ understanding of their roles impacts their professional advancement, essential for developing and nurturing children. We gathered data from six kindergartens in G city, employing a multiple linear regression model to scrutinize factors influencing this relationship. Our approach involved separate hypothesis formulation and model building, complemented by validity checks via endogeneity and robustness tests using advanced statistical methods. The analysis highlighted the importance of teachers’ emotional connection to their work, with a notable regression coefficient of 0.403. It demonstrated that enhancing core literacy by 1% leads to a 0.154% increase in professional development levels. The findings advocate focusing on emotional engagement and core skills to foster excellence in early childhood education.

Актуальные вопросы и вызовы правовой статистики России

The article examines current issues and challenges of legal statistics in Russia. In modern Russia, statistics play a key role in the development of all aspects of public administration and the legal system, where the accuracy, objectivity and completeness of the collected data become the cornerstones for making informed decisions, forming legal policy and effectively regulating public relations. The use of advanced statistical analysis methods and improving the quality of statistical data will not only increase the effectiveness of law enforcement and judicial proceedings, but also contribute to strengthening the legal system, increasing public confidence in authorities and the judicial system.

О выборе метода динамического нормирования энергоресурсов нефтеперерабатывающих производств

The article discusses the possibility of calculating the expected energy demand based on big data and machine learning for the energy technological processes in oil refineries. In order to obtain predictive data, linear regression, machine learning, and neural networks are proposed to be used to build a mathematical model. The advantages and disadvantages of these methods are discussed, and the accuracy of the models is compared with the possibility of interpreting them. Thanks to the use of advanced statistical methods, the variability of energy consumption can be interpreted through factor analysis. Through pilot tests, the practical significance of these proposed methods for their practical use in an energy management system is demonstrated, as well as the transition to statistical control of the process.

Quantitative fusion of NLP, fMRI, and EEG data : A mathematical model for decoding semantic processing in the brain

This research introduces a novel mathematical model designed to integrate Natural Language Processing (NLP), functional Magnetic Resonance Imaging (fMRI), and Electroencephalography (EEG) data, aiming to decode the complex neural mechanisms of semantic processing in the human brain. By leveraging the complementary strengths of each modality—NLP’s linguistic analysis, fMRI’s spatial resolution, and EEG’s temporal precision—the model provides a groundbreaking approach to understanding how semantic information is processed across different brain regions and over time. The core of the proposed model is a dynamic, multi-layered framework that utilizes advanced statistical methods and machine learning algorithms. At its foundation, the model employs vector space representations from NLP to quantify semantic similarity and contextuality in language. These representations are then mapped onto neural activation patterns captured by fMRI and EEG, using a series of transformation matrices that are optimized through machine learning techniques. The model uniquely incorporates time-series analysis to account for the temporal dynamics of EEG data, while spatial patterns from fMRI data are analyzed through convolutional neural networks, ensuring a comprehensive integration of multimodal neuroimaging data. Key to proposed approach is the application of Bayesian inference methods to fuse these diverse data sources, allowing for the probabilistic modeling of semantic processing pathways in the brain. This enables the prediction of neural responses to linguistic stimuli with unprecedented accuracy and detail. Theoretical implications of our model suggest significant advances in understanding the neural basis of language comprehension, offering new insights into the dynamic interplay between linguistic structures and neural processes.

Healing Voices: A Deep Dive into Doctor Perceptions on relation between Malpractice and Medical Legislation and Healthcare system in Romania. The results of a qualitative sociological research

This article presents findings from a comprehensive survey titled “Dimensions of Malpractice in Romania” addressing the negative aspects of the current legislation on professional liability. The data reveals a notable gap in doctors' knowledge of the legislative framework specific to the medical profession, emphasizing a high demand for formal, institutional training. The prevalence of "I don't know" responses suggests a conceptual gap, possibly stemming from ultra-specialized training that lacks trans-disciplinary education, particularly in legal matters. Medical malpractice emerges as a significant challenge for both the healthcare system and society at large. This survey delves into the complex social context of medical practice, exploring individual experiences and social influences. The large participant pool (8,121 participants, with 4,729 responses) enhances the research's scalability, allowing for the identification of prevalent themes and subsequent correlations through advanced statistical methods. The study, conducted through a questionnaire administered within a month to over 8,000 participants, highlights the cost-effectiveness and efficiency of survey research compared to interview-based approaches. Despite its advantages, the qualitative nature of the study presents limitations, such as the potential subjectivity in data interpretation. Limitations include the potential lack of generalizability, leading to the organization of data based on frequency rather than broad applicability. The findings may be more relevant for specific contexts, requiring further evaluations and independent assessments. This qualitative sociological research prioritizes an in-depth understanding of phenomena over quantitative aspects. The identified themes and sub-themes reflect negative conditions and vulnerabilities in medical practice, serving as a basis for potential amendments to legal texts. A legislative framework adapted to local social conditions is proposed to benefit both the healthcare system and broader society. The article concludes with a call for continued research, emphasizing the importance of refining the legislative framework to enhance the overall healthcare landscape in Romania.