Pareto Principle Research Articles

Comprehensive Statistical Analysis of ISSN Records

The history of the origin and transformation of the ISSN Register is considered, and all publications available through Google Scholar and Semantic Scholar on the number of ISSN records and their dynamics are collected together. Data on the number of entries in the ISSN Register by country for 2014 and 2021 is presented, along with the seven-year increase in these entries in comparison with similar data for 1991 through 1998. From these data, the top ten countries were analyzed based on the maximum increase in records over these two seven-year periods. It is shown that the first list of countries with the maximum increase in the number of ISSN records for 2014 through 2021 included exclusively developing countries, and the second list (1991–1998) included mainly developed countries. Similar increases in the number of ISSN records were analyzed for the languages of serial publications and it was shown that in both language lists there was only the Turkish language and the leading European languages were absent. It is shown that the total contributions of ISSN records of leading countries and languages to the total number of ISSN records satisfy the Pareto principle for all years studied. The calculations were made on the indices of linguistic liberalization of the information space of states and information and linguistic influence (or language expansion) according to ISSN indicators. It is shown that aggregating countries by language has made it possible to significantly reduce the discrepancy between the total ISSN records for countries in which a particular language predominates and the ISSN records for a given language. These differences for calculations for 2014 and 2021 levels did not exceed 9% for English, Spanish and Portuguese, and 14% for French, which corresponds to the languages of former colonial empires. In conclusion, a linear regression relationship between ISSN/10 thousand people and GDP per capita with moderate values of the correlation coefficient was obtained in two versions, which indirectly confirms the Constancy Hypothesis.

UAV path planning based on improved dung beetle algorithm with multiple strategy integration

This research presents a multi-strategy augmented dung beetle algorithm (CDBO) for UAV path planning in intricate 3D environments. Its main objective is to address the path planning challenge by formulating an integrated cost function model and environment representation that aligns the optimisation task with the navigation prerequisites and safety constraints of the UAV. Firstly, the algorithm initialises the particle population based on the Lévy flight principle to improve the species diversity and adequately search the solution space. Subsequently, an exponentially decreasing inertia weighting strategy is introduced to improve the convergence speed of the algorithm. In addition, the algorithm uses an adaptive [Formula: see text]-distribution with perturbed variation of the updated positions so that the algorithm avoids falling into a local optimum. Finally, to enhance the robustness of the system, the algorithm incorporates an elite retention strategy based on the Pareto principle. In this study, rigorous tests using 12 CEC2022 test functions and Wilcoxon rank sum test are conducted as benchmarks for the performance of the algorithm. The results show that the enhanced dung beetle algorithm outperforms the traditional algorithm, highlighting its efficiency and robustness. In addition, the efficacy of the enhanced dung beetle algorithm was validated in a variety of complex 3D environments, affirming its ability to generate optimal paths. This study further highlights the importance of effective algorithms in UAV path planning, addressing the key issue of optimising flight operations in intricate spatial scenarios.

Incommensurability, the sequence argument, and the Pareto principle

AbstractParfit (Theoria 82:110–127, 2016) responded to the Sequence Argument for the Repugnant Conclusion by introducing imprecise equality. However, Parfit’s notion of imprecise equality lacked structure. Hájek and Rabinowicz (2022) improved on Parfit’s proposal in this regard, by introducing a notion of degrees of incommensurability. Although Hájek and Rabinowicz’s proposal is a step forward, and may help solve many paradoxes, it can only avoid the Repugnant Conclusion at great cost. First, there is a sequential argument for the Repugnant Conclusion that uses weaker and intuitively more compelling assumptions than the Sequence Argument, and which Hájek and Rabinowicz’s proposal only undermines, in a principled way, by allowing for implausible weight to be put on the disvalue of inequality. Second, if Hájek and Rabinowicz do put such implausible weight on the disvalue of inequality, then they will have to accept that a population A is not worse than another same sized population B even though everyone in B is better off than anyone in A.

WHO Surgical Safety Checklist compliance rate at a large tertiary hospital in Vietnam.

The WHO Surgical Safety Checklist has been instrumental in reducing surgical deaths by 47% globally since its inception in 2009. A study was conducted to assess the compliance of hospital staff with the Surgical Safety Checklist and identify potential barriers to its implementation at a large tertiary public hospital in Vietnam. The study analysed 135 surgical cases between February and September 2021 using the Pareto principle with eight error categories, including (1) patient identification, (2) equipment, (3) risk management, (4) blood loss, (5) allergy, (6) items left inside the patient, (7) surgical specimens and (8) patient safety and recovery management. Compliance with the Surgical Safety Checklist was between 77% and 93%, with five categories accounting for more than 80% of errors. Periodic monitoring and staff training can improve compliance and help reduce errors for better patient safety. Long-term solutions addressing infrastructure challenges are also crucial.

Pareto-principle in rare disease education: assessing the representation of common rare diseases in medical education and coding systems

BackgroundThe Pareto Principle asserts that a large portion of results can be achieved with a small amount of effort. Wakap et al. found that around 80% of individuals with rare diseases (RD) suffer from one of 149 specific rare diseases. A significant challenge in the RD domain is the lack of information, compounded by the fact that most RD are not specifically codifiable in the ICD-10, leading to a deficit in reliable epidemiological data. Additionally, time constraints in medical education hinder the comprehensive teaching of all RD, contributing to the diagnostic odyssey problem through failure of recognizing diseases. We identified the most and second most prevalent RD (prevalences of 1–5/10,000 and 1–9/100,000, respectively) from the Orphanet Epidemiology File, totaling 454 diseases. We investigated the feasibility of specific coding using ICD-10-GM and whether these diseases were explicitly listed in the subject catalog (GK) of the second state examination in human medicine in Germany. A two-sided chi-square test was employed to identify statistically significant differences between prevalence groups.ResultsOut of 454 diseases, a total of 34% could be specifically coded in ICD-10-GM, with 49% of diseases in the 1–5/10,000 prevalence range (153 RD) and 26% in the 1–9/100,000 range (301 RD) having specific codes. Approximately 15% of all investigated diseases were part of the GK, with 25% of the most prevalent and 10% of the second most prevalent RD group, respectively. Statistically significant differences were observed between prevalence groups concerning the presence of a specific ICD-10-GM code and inclusion in the GK.ConclusionOnly 49% of the most prevalent RD can be specifically coded, highlighting the challenge of limited epidemiological data on RD. In Germany, the Alpha-ID was introduced in addition to ICD-10 in the inpatient setting to obtain more valid epidemiological data on RD. Recognizing the Pareto Principle’s applicability, the study emphasizes the importance of including the most common rare diseases in medical education. While recognizing the limitations, especially in covering ultra-rare diseases, the study underscores the potential benefits of enhancing medical curricula to improve rare disease awareness and diagnostic accuracy.

Multi-objective topology optimization of macro structure and microtubule network structure for self-healing material

Abstract Self-healing materials possess the capability to promptly repair minor damages occurring during service, thereby effectively preventing safety accidents. This paper investigates a multi-objective topology optimization method for the macro structure and microtubule network of self-healing materials around pure epoxy resin materials, aiming to enhance the damage healing capability of the microtubule network while meeting the mechanical performance requirements of the macro structure. By introducing the design variables of macro structure and microtubule network, the corresponding topological description functions are established respectively. And study applies logical operations and post-processing techniques to generate an embedded microtubule network structure description. The objective functions include the flexibility of the macro structure, the along-travel head loss, and the total length of the microtubule network, with material volume serving as a constraint. In order to determine the head loss of the three-dimensional microtubule network structure, a Hardy-Cross method based on flow initialization and loop search is proposed. Multi-objective topology optimization is designed based on moving morphable components algorithm, enumeration method and Pareto principle. Develop iterative termination conditions by assessing the disparity between Pareto solution sets in each generation, thereby ensuring algorithm convergence. The numerical example of the Messerschmitt–Bölkow–Blohm (MBB) beamyields a flexibility of 0.059 without a carrier and 0.0728 with a carrier the macrostructural flexibility without a carrier is 81.0% compared to with a carrier, and the macrostructural profiles and the overall flexibility of the MBB beams with/without a carrier are close to each other. This method serves as a reference for optimizing large-scale self-healing structures.

GERENCIAMENTO DE RISCOS NO PLANEJAMENTO DAS LICITAÇÕES E CONTRATOS PÚBLICOS

Recent legislative changes in the area of public bidding and contracts, especially the guidelines implemented by Normative Instruction SEGES/MPDG Nº. 05/2017, established a new regime for contracting outsourced services in the Brazilian legal system. This new instrument aims to expand the dispute, in compliance with the principle of competitiveness, resulting in the expansion of the supplier market. IN Nº. 05/2017 establishes the rules and guidelines for the procedure for contracting services under the indirect execution regime within the scope of the Direct, autarchic and foundational Federal Public Administration. The regulation establishes innovative phases: Contracting Planning; Supplier Selection; and Contract Management. The Planning phase consists, according to the IN, of 3 stages: Preliminary Studies; Risk Management; and Terms of Reference or Basic Design. This paper aims to present a Risk Management Plan, developed in the planning phase of contracting a bidding process in the Electronic Auction for Price Registration modality. According to the PMBOK® Guide Sixth Edition p. 395, project risk management includes the processes of conducting planning, identification, analysis, response planning, implementation of responses and monitoring of risks in a project. The objective is to maximize exposure to positive events and minimize exposure to negative events. In this context, through causes, consequences, categories and SWOT analysis, 75 risks were identified. Risk management processes were used in accordance with the PMBOK® doctrine and the Pareto Principle to prioritize 25 risks.

Land suitability and human adaptation: River Siang from Sangam to Pongging, Arunachal Pradesh, India

Land suitability in mountainous regions profoundly influences human adaptation strategies, particularly in environments shaped by morphometric characteristics. This study focuses on the Siang river valley from Sangam to Pongging, Arunachal Pradesh, India, employing Land Suitability Analysis (LSA) to assess terrain viability for settlement, and orchard cultivation. Key determinants such as topographic features, drainage morphometry, and infrastructural accessibility were analyzed to develop thematic maps delineating optimal zones for different land uses. The research underscores how geomorphic attributes and accessibility influence settlement patterns and orchard suitability, with lower elevations proving favorable for settlements and orchards due to accessible topography and climatic conditions. By integrating spatial analysis techniques and assessing variables like elevation, slope, and proximity to infrastructure by euclidian distance and pareto principle (80/20 rule), this research provides a comprehensive framework for informed land use planning. Strategic planning recommendations emphasize sustainable practices and community engagement to balance development needs with environmental preservation in Arunachal Pradesh's rugged terrain. It advocates for collaborative efforts between stakeholders to optimize land utilization and foster resilient development amidst challenging environmental conditions. This initial attempt aims to raise awareness among land users, planners, research workers, and administrators to ensure proper and effective land management.

Determinants of Downloads as Demand for Hybrid Journals

Although Big Deal contracts that provide access rights to all electronic journals published by the publisher initially gained favor with university libraries, some libraries have terminated these contracts owing to increased charges since the 2010s. Consequently, they are faced with the problem of selecting journals for purchase within their limited budgets. This study investigates the factors affecting the number of downloads, representing journal demand, to provide libraries with guidance on journal selection. The download equation for 1485 hybrid journals published by Springer Nature is formulated using ordinary least squares. The results found that 5% and 50% of the 1485 journals generated approximately 30% and 85% of the downloads in 2022, respectively. Downloads are concentrated in fewer journals, although the Pareto principle does not apply to hybrid journals. Demand concentration implies that libraries do not need to maintain access rights to all journals. Recently, a few leading publishers have provided access rights to almost all electronic journals based on transformative agreements aiming to promote open access. Therefore, this study’s findings raise the issue of the rationale for bundling electronic journals in transformative agreements, which is similar to Big Deal. Moreover, the results of the download estimation reveal that hybrid journals with more open access articles, larger citation scores, and longer histories acquire more downloads. These findings indicate that open access accelerates the dissemination of research.

Enhanced Filtrate Feature Selection Algorithm for Feature Subset Generation

Objectives: In the bioinformatics field feature selection plays a vital role in selecting relevant features for making better decisions and assessment of disease diagnosis. Brain Tumour (BT) is the second leading disease in the world. Most of the BT detection techniques are based on Magnetic Resonance (MR) images. Methods: In this paper, medical reports are used in the detection of BT to increase the surveillance of patients. To improve the accuracy of predictive models, a new adaptive technique called Enhanced Filtrate Feature Selection (EFFS) algorithm for optimal feature selection is proposed. Initially, the EFFS algorithm finds the dependency of each attribute and feature score by using Mutual Information Gain, Chi-Square, Correlation, and Fishore score filter methods. Afterward, the occurrence rate of each top-ranked attribute is filtered by applying threshold value and obtaining the optimal feature by using the Pareto principle. Findings: The performance of the selected optimal features is evaluated by time complexity, number of features selected, and accuracy. The efficiency of the proposed algorithm is measured and analyzed in a high-quality optimal subset based on a Random Forest classifier integrated with the ranking of attributes. The EFFS algorithm selects 39 out of 46 significant and relevant features with minimum selection time and shows 99.31 % of accuracy for BT, 29 features with 99.47% of accuracy for Breast Cancer, 15 features with 94.61% of accuracy for Lung Cancer, 15 features with 98.84% of accuracy for Diabetes and 43 features with 90% of accuracy for Covid-19 dataset. Novelty: To decrease the processing time and improve the performance of a model feature selection process will be done at the initial stages for the betterment of the classification task. Thus, the proposed EFFS algorithm is applied to different datasets based on medical reports and EFFS outperforms with greater performance measurements and time. The appropriate feature selection techniques help to diagnose diseases in the prior phase and increase the survival of human beings. Keywords: Bioinformatics, Brain Tumour, Chi­Square, Correlation, EFFS, Feature Selection, Fishore Score, Information Gain, Optimal Features, Random Forest

Implementation of the ABC Analysis to the Inventory Management

In an ever-changing market, inventory management is becoming a major problem that requires thorough planning, supervision, and control. If companies are well managed and have good planning and control, they can last a long time. Poor inventory management is very detrimental to the business as it can cause the company to fail to meet customer or consumer demands, which can lead customers to choose other companies that can meet their demands. Plan your stockpiles to maintain balance, avoid excess or deficiency, and control storage costs. ABC analysis, one of the most common inventory management techniques, divides inventory into three categories (A, B, and C) based on total annual product usage or total inventory cost based on the Pareto principle. The goal of this study is to use ABC analysis in the company's inventory management to identify products in categories A, B, and C based on profitability and sales factors. The research phases include data collection, data preprocessing, exploratory data analysis, ABC analysis, key product analysis, and explanatory analysis. According to the research findings, Segment A accounts for 18% of the total product and generates 78% of the overall profit. Segment B contributes 23% of the total number of items and 16% of all revenue.

IMPLEMENTASI PRINSIP PARETO PADA PEKERJAAN PERBAIKAN DI PROYEK PERUMAHAN

Rework in construction refers to the activity of repairing or redoing work that does not meet quality standards, causing delays and increased costs. The aim of this research is to identify the causes of rework in building structures, with a case study of four types of houses in one of the housing complexes in South Tangerang City. This research focuses on the use of quality improvement tools such as Pareto Charts. Pareto diagrams, based on the Pareto principle which states that most consequences come from a small number of causes, are used to identify and prioritize the causes of rework. The types of work that most frequently result in rework will be monitored more closely to minimize future errors. Data collection through field observations by calculating the frequency of repair work. The results of the analysis show that most rework is caused by some type of repeated error. The implementation of Pareto resulted in 75% of rework resulting from 50% of the causes. There were 5 causes of rework occurring which were analyzed, namely inappropriate dimensions, elevation differences, slanted formwork, ends of reinforcement that were not connected, and reinforcement that needed cleaning. This implementation helps in solving these 5 problems and provides lessons for other similar projects

Factual Analysis of Factors Influencing Consumer Cognitive Thinking and Automobile Designing using Fuzzy-AHP

Consumer cognitive and emotional responses significantly impact consumers’ affinity for both tangible and intangible products. Recent research underscores the heightened influence of consumer emotional and cognitive perceptions in product design, surpassing reliance on designers’ instincts or experiences. This case study delved into the analysis of automobiles, using cars as focal product. While visual elements like curve lines, grill design, and color predominantly shape consumer perspectives, non-visual factors such as reliability, quality, and ergonomics play a pivotal role in purchasing decisions. Our investigation, involving six industrial experts and over 130 participants, explored non-visual factors affecting consumers’ cognitive perceptions during car purchasing. Through a two-phase experimental approach, we developed a framework to understand the disparities in ranking assigned by consumers and designers to these non-visual factors, revealing perception gaps. Utilizing the rank value methodology, the Pareto principle, and Fuzzy-AHP analysis, we identified mileage/fuel efficiency, safety features, and reliability as dominant factors influencing consumer perceptions. Designers, on the other hand, prioritize safety features, reliability, and quality/warranty. Our findings emphasize the strategic significance for automobile companies to enhance their success rates by prioritizing these key factors, ultimately elevating brand value, profile, and overall financial prosperity.

Fault prioritisation for Air Handling units using fault modelling and actual fault occurrence data

Fault impact analysis is an important step in developing Fault Detection and Diagnosis (FDD) tools for Heating, Ventilation and Air-Conditioning (HVAC) systems. It provides an insight into how HVAC systems react in the presence of operational faults and helps to prioritise which faults to focus on. The research in the existing literature has mostly focused on modelling several faults occurring in various HVAC systems and components to evaluate the effect of the fault on energy performance and the predicted thermal comfort of occupants. However, the real frequency of fault occurrence, an important factor which affects the overall impact a fault has on the system’s performance, has barely been addressed in the reviewed literature. This paper addresses this gap by considering real fault occurrence frequency data in addition to the effect of the faults on the energy performance of the building to assess their total energy impact. A real office building in the Netherlands was modelled using DesignBuilder and its energy performance was simulated using EnergyPlus. Air Handling Unit (AHU) faults were introduced using the native fault object and Energy Management System (EMS) features available in EnergyPlus. The fault occurrence data was extracted from AHU work orders using text mining. The fault modelling and text mining results were combined to get the total energy impact of the fault and, subsequently, the faults were prioritised using the Pareto principle. The research identified that fan failure, Heat Recovery Wheel (HRW) failure, fan stuck at 50% and Heating Coil Valve (HCV) stuck at 0% are the priority faults for winter and fan failure is the priority fault for summer.

Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis

The subject of this article is endocrinological diseases, namely, the analysis of complications in people with hypothyroidism and hyperthyroidism. It is known that these diseases occur asymptomatically or in a way that may indicate other possible diseases, so people do not suspect what exactly they are suffering from. Later, the diseases develop to the point where complications occur in the body, some of the most dangerous of which are psychological disorders: depression, mania, aggression, etc. Therefore, the aim of this work is to develop methods for predicting the occurrence of neurological deterioration in people who have already been diagnosed with endocrinological diseases. The article solves the problem of choosing the best models for predicting the occurrence of psychological disorders in people with endocrinological problems. Machine learning methods that are widespread in the medical field were analyzed and one of them was chosen that more optimally solves all the tasks of the task. The selection of criteria took into account potential problems with medical and psychological data. The method used was linear additive convolution, which is used to select the best alternatives according to the results, with the Pareto principle, which aims to exclude less suitable alternatives because all the features have lower values than in other options. For the experiment, all features were converted into quantitative ones to calculate convolution values. The evaluation criteria are given in the paper. The following results were obtained: the forecasting model in further study of this problem will be a random forest. Conclusions: the forecasting methods were studied and a more optimal model was chosen using linear additive convolution, namely, the random forest algorithm, its advantages and disadvantages were considered. A more detailed analysis of its development will be presented in the following articles. A mathematical description of the chosen forecasting method is provided, which includes potential ways of implementation and steps for building an algorithm for one of these methods.

Dynamic analysis of the IEASWR information propagation model with Holling-type II functional response

Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number [Formula: see text] is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation.

Repugnance at the limit

AbstractThe implications of the repugnant conclusion for consequentialist theories, such as total utilitarianism, have been extensively discussed since the work of Derek Parfit. These discussions make the paradoxes of population ethics depend on welfarist conditions, like the dominance condition (or the Pareto Principle). Thus, one might think that the repugnant conclusion is not a practical problem for deontologists, who deny that we always ought to do what produces the most aggregate welfare. In this study, we offer two impossibility results using what we call the limit aggregation property. This states that when welfaristic and non‐welfaristic considerations conflict, there is a threshold number of persons such that the former can override the latter as long as more people than the threshold number can enjoy increases in well‐being levels. We argue that this property should be accepted even by deontologists, insofar as they do not commit themselves to the implausible absolutist position that fails to assign any moral weight to aggregate welfare. Our results therefore state that any normative population theory that is not absolutist entails (a variant of) the repugnant conclusion or some other implausible conclusion. Therefore, the repugnant conclusion must be taken seriously even by non‐absolutist deontologists, not just consequentialists.

A Structural Framework for Effective Time Management in Dynamic Work Environments

This paper presents a structural framework to enhance time management profi- ciency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The evaluation demonstrates significant improvements in time management efficiency and client satisfaction across various professional contexts. For instance, by employing the eisenhower Matrix and Pareto Principle, project managers achieved a 20% im- provement in project completion times. The framework’s adaptability is further highlighted by a 25% reduction in project turnaround time in a marketing agency and a 30% increase in project visibility in a startup. These findings underscore the framework’s practical implementation as a holistic approach to managing time effectively and achieving long-term success. Continuous refinement, real- time feedback integration, and exploring the impact of emerging technologies are recommended for further enhancing the framework’s effectiveness. This research contributes valuable insights for organizations aiming to navigate the complexities of modern work environments.

Knowledge, attitudes, and practices of primary healthcare practitioners regarding pharmacist clinics: a cross-sectional study in Shanghai

BackgroundPharmacist clinics offer professional pharmaceutical services that can improve public health outcomes. However, primary healthcare staff in China face various barriers and challenges in implementing such clinics. To identify existing problems and provide recommendations for the implementation of pharmacist clinics, this study aims to assess the knowledge, attitudes, and practices of pharmacist clinics among primary healthcare providers.MethodsA cross-sectional survey based on the Knowledge-Attitude-Practice (KAP) model, was conducted in community health centers (CHCs) and private hospitals in Shanghai, China in May, 2023. Descriptive analytics and the Pareto principle were used to multiple-answer questions. Chi-square test, Fisher’s exact test, and binary logistic regression models were employed to identify factors associated with the knowledge, attitudes, and practices of pharmacist clinics.ResultsA total of 223 primary practitioners participated in the survey. Our study revealed that most of them had limited knowledge (60.1%, n = 134) but a positive attitude (82.9%, n = 185) towards pharmacist clinics, with only 17.0% (n = 38) having implemented them. The primary goal of pharmacist clinics was to provide comprehensive medication guidance (31.5%, n = 200), with medication education (26.3%, n = 202) being the primary service, and special populations (24.5%, n = 153) identified as key recipients. Logistic regression analysis revealed that education, age, occupation, position, work seniority, and institution significantly influenced their perceptions. Practitioners with bachelor’s degrees, for instance, were more likely than those with less education to recognize the importance of pharmacist clinics in medication guidance (aOR: 7.130, 95%CI: 1.809–28.099, p-value = 0.005) and prescription reviews (aOR: 4.675, 95% CI: 1.548–14.112, p-value = 0.006). Additionally, practitioners expressed positive attitudes but low confidence, with only 33.3% (n = 74) feeling confident in implementation. The confidence levels of male practitioners surpassed those of female practitioners (p-value = 0.037), and practitioners from community health centers (CHCs) exhibited higher confidence compared to their counterparts in private hospitals (p-value = 0.008). Joint physician-pharmacist clinics (36.8%, n = 82) through collaboration with medical institutions (52.0%, n = 116) emerged as the favored modality. Daily sessions were preferred (38.5%, n = 86), and both registration and pharmacy service fees were considered appropriate for payment (42.2%, n = 94). The primary challenge identified was high outpatient workload (30.9%, n = 69).ConclusionsAlthough primary healthcare practitioners held positive attitudes towards pharmacist clinics, limited knowledge, low confidence, and high workload contributed to the scarcity of their implementation. Practitioners with diverse sociodemographic characteristics, such as education, age, and institution, showed varying perceptions and practices regarding pharmacist clinics.

International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS

This paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies.