Transparent Process Research Articles

A Comprehensive Framework for Pathology Classification Bridging Precision and Interpretability

Pathology classification is an indispensable component of medical diagnostics, facilitating accurate disease identification, prognosis determination, and treatment planning. However, the increasing complexity and heterogeneity of pathological manifestations pose significant challenges to traditional classification methodologies. This abstract presents a novel framework that integrates advanced machine learning techniques with domain-specific expertise to enhance the precision and interpretability of pathology classification. Our framework adopts a multi-modal approach, leveraging diverse data sources including histopathological images, clinical records, genomic profiles, and molecular biomarkers. Through feature fusion and dimensionality reduction techniques, we effectively capture intricate patterns and latent relationships embedded within the data, enabling robust classification across diverse pathological conditions. Furthermore, interpretability is prioritized through the incorporation of explainable AI methodologies, facilitating the identification of salient features and decision rationales underlying classification outcomes. This ensures transparency and trustworthiness in the diagnostic process, empowering clinicians to make informed decisions and refine treatment strategies. Validation of our framework across various pathological contexts demonstrates superior performance compared to conventional approaches, exhibiting high accuracy, sensitivity, and specificity. Moreover, its modular architecture facilitates customization and scalability, accommodating evolving diagnostic needs and emerging technological advancements. In conclusion, our proposed framework represents a significant advancement in pathology classification, offering a synergistic blend of computational sophistication and clinical relevance. By seamlessly integrating cutting-edge technologies with domain knowledge, it holds promise for revolutionizing diagnostic practices and improving patient outcomes in the realm of precision medicine.

Critical Success Factors for Public-Private Partnerships in the Development of Mega Power Generation Infrastructures in Sri Lanka

Ceylon Electricity Board (CEB) seeks alternative financing through Public Private Partnerships (PPP) due to non-cost-reflective tariff losses and debt overhang, relying on maturity in critical success factors. Thus, the research examines the success factors of Public Private Partnerships in developing power generation infrastructures in Sri Lanka, using quantitative data from a questionnaire distributed to stakeholders in CEB. The data analysis tools of descriptive and inferential statistics were used to analyze the collected data. The economic viability of the project, credibility of government policies, legal and regulatory framework, equitable risk allocation, transparent and efficient procurement process, financial market, a strong and good private consortium, and political stability, were found as the main CSFs for the development of mega power plants. The study was concentrated on the public agency of CEB, for generalization investigation to be extended to private investors, practitioners, and policymakers in the power sector. Further, a comprehensive study on the exploration of CSF to attract private investors to PPP for the development of mega power plants in Sri Lanka would be most beneficial to the industry. The framework developed in this research would be the benchmark for identifying critical success factors for improvement in PPP projects in Sri Lanka.

Examining the Integration of Responsible AI Principles in Social Media Marketing for Digital Health: A Theoretical Analysis

This study explores the integration of responsible artificial intelligence (AI) principles into social media marketing strategies within the digital health sector. With the proliferation of digital health platforms and the growing use of AI technologies in marketing, ensuring ethical practices is paramount. By examining the application of responsible AI principles in this context, this research aims to address concerns related to privacy, fairness, transparency, and accountability. Through a comprehensive analysis of current practices and emerging trends, this study highlights the importance of balancing marketing objectives with ethical considerations to promote trust and engagement among users. Key aspects include leveraging AI algorithms for personalized content delivery while safeguarding user data, implementing transparent AI-driven decision-making processes, and establishing mechanisms for accountability and user empowerment. This investigation offers valuable insights for marketers, policymakers, and stakeholders in navigating the evolving landscape of digital health marketing while upholding ethical standards and fostering positive user experiences. Key Words: Responsible AI, Social Media Marketing, Digital Health, Ethical Practices, Transparency

A comprehensive analysis of digital health-focused Living Labs: innovative approaches to dementia.

The increasing prevalence of dementia demands innovative solutions; however, existing technological products often lack tailored support for individuals living with this condition. The Living Lab approach, as a collaborative innovation method, holds promise in addressing this issue by actively involving end-users in the design and development of solutions adapted to their needs. Despite this potential, the approach still faces challenges due to its lack of recognition as a research methodology and its absence of tailored guidelines, particularly in dementia care, prompting inquiries into its effectiveness. This narrative review aims to fill this gap by identifying and analysing digital health Living Labs focusing on dementia solutions. Additionally, it proposes guidelines for enhancing their operations, ensuring sustainability, scalability, and greater impact on dementia care. Fifteen Living Labs were identified and analyzed. Based on trends, best practices, and literature, the guidelines emphasize user engagement, interdisciplinary collaboration, technological infrastructure, regulatory compliance, transparent innovation processes, impact measurement, sustainability, scalability, dissemination, and financial management. Implementing these guidelines can enhance the effectiveness and long-term impact of Living Labs in dementia care, fostering new collaborations globally.

Factors influencing the development, recruitment, integration, retention and career development of advanced practice providers in hospital health care teams: a scoping review

BackgroundAdvanced practice providers (APPs), including physician assistants/associates (PAs), nurse practitioners (NPs) and other non-physician roles, have been developed largely to meet changing healthcare demand and increasing workforce shortages. First introduced in primary care in the US, APPs are prevalent in secondary care across different specialty areas in different countries around the world. In this scoping review, we aimed to summarise the factors influencing the development, recruitment, integration, retention and career development of APP roles in hospital health care teams.MethodsWe conducted a scoping review and searched Ovid MEDLINE, Ovid Embase, Ovid Global Health, Ovid PsycINFO and EBSCOhost CINAHL to obtain relevant articles published between Jan 2000 and Apr 2023 that focused on workforce management of APP roles in secondary care. Articles were screened by two reviewers independently. Data from included articles were charted and coded iteratively to summarise factors influencing APP development, recruitment, integration, retention and career development across different health system structural levels (macro-, meso- and micro-level).ResultsWe identified and analysed 273 articles that originated mostly from high-income countries, e.g. the US (n = 115) and the UK (n = 52), and primarily focused on NP (n = 183) and PA (n = 41). At the macro-level, broader workforce supply, national/regional workforce policies such as work-hour restrictions on physicians, APP scope of practice regulations, and views of external collaborators, stakeholders and public representation of APPs influenced organisations’ decisions on developing and managing APP roles. At the meso-level, organisational and departmental characteristics, organisational planning, strategy and policy, availability of resources, local experiences and evidence as well as views and perceptions of local organisational leaders, champions and other departments influenced all stages of APP role management. Lastly at the micro-level, individual APPs’ backgrounds and characteristics, clinical team members’ perceptions, understanding and relationship with APP roles, and patient perceptions and preferences also influenced how APPs are developed, integrated and retained.ConclusionsWe summarised a wide range of factors influencing APP role development and management in secondary care teams. We highlighted the importance for organisations to develop context-specific workforce solutions and strategies with long-term investment, significant resource input and transparent processes to tackle evolving healthcare challenges.

Guiding Principles for Evaluating Vaccines in Joint Health Technology Assessment in the European Union: Preparing for the European Union’s Regulation on Health Technology Assessment for Vaccines

ObjectivesThe appraisal of vaccines in the European Union (EU) currently involves many different decision-making bodies and processes. The objective of this study was to help inform the development of standardized methodology and vaccine-specific processes for use in the EU Regulation on health technology assessment (HTA). MethodsLiterature reviews and expert consultation were conducted to identify current practices and gaps related to vaccine appraisals and to develop guiding principles for the joint clinical assessment of vaccines. ResultsWe found that significant variation exists across the EU member states in the decision-making processes when clinically evaluating vaccines. Three guiding principles consisting of 13 recommendations were developed to help inform the development of decision-making frameworks for the joint clinical assessment of vaccines in the EU: (1) support the creation of appropriate terminology and measurements for clinical appraisals of vaccines; (2) develop inclusive, timely, and transparent vaccine appraisal processes to support stronger evidence generation for vaccine decision making and appraisal; and (3) improve the collection and interoperability of real-world data, including robust surveillance, to foster evidence generation and support the standardization of vaccine clinical appraisals. ConclusionsGiven the significance of vaccines for public health, there is an urgency to develop standardized and vaccine-specific methodologies and processes for use in the EU joint HTA framework. The proposed guiding principles could support the effective implementation of the EU Regulation on HTA for vaccines and have the potential to ensure consistent, transparent, and timely access to new vaccines in the EU.

Research integrity in the era of artificial intelligence: Challenges and responses.

The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.

Shannon Holes, Black Holes, and Knowledge: The Essential Tension for Autonomous Human–Machine Teams Facing Uncertainty

We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as teammates, artificial intelligence (AI) machines must be able to determine what constitutes the usable knowledge that contributes to a team’s success when facing uncertainty in the field (e.g., testing “knowledge” in the field with debate; identifying new knowledge; using knowledge to innovate), its failure (e.g., troubleshooting; identifying weaknesses; discovering vulnerabilities; exploitation using deception), and feeding the results back to users and society. It matters not whether a debate is public, private, or unexpressed by an individual human or machine agent acting alone; regardless, in this exploration, we speculate that only a transparent process advances the science of autonomous human–machine teams, assists in interpretable machine learning, and allows a free people and their machines to co-evolve. The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric. We conclude that the structure of “knowledge”, once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its useful applications are multifaceted (akin to maximum entropy production). Our novel finding is the existence of Shannon holes that are gaps in knowledge, a surprising “discovery” to only find Shannon there first.

Comparative Analysis of Upper Limb Impairment Rating Processes in the AMA Guides Sixth Edition 2024 vs 2008: Impact on Stakeholders.

To evaluate how upper limb impairment ratings are affected by updates to the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides) Sixth Edition 2024 compared with the AMA Guides Sixth Edition 2008, and to investigate potential correlations with judicial, legislative, and economic factors. Two expert evaluators reviewed 31 upper limb clinical vignettes from the 2008 and 2024 versions. The impairment ratings for each version were compared. After following the impairment ratings steps in each version, the results for 2024 showed no significant differences when compared with the impairment ratings generated using 2008 methods (Cohen's Kappa = 1.00). The updated AMA Guides Sixth Edition 2024 with improved, transparent processes and enhanced diagnosis-based impairment tables provides efficiency while retaining the accuracy, validity, and reliability of past versions of the AMA Guides.

O-221 An interpretable deep learning model for predicting the number of mature oocytes retrieved after ovarian hyperstimulation

Abstract Study question Can an interpretable deep learning model successfully predict the number of mature oocytes retrieved after ovarian hyperstimulation and provide the rationale for these predictions? Summary answer An interpretable deep learning model can successfully predict the number of mature oocytes retrieved and provide a rationale for these predictions. What is known already A number of recent studies have used classical machine learning models in order to predict the number of mature oocytes retrieved in IVF cycles, including XGBoost, RandomForests and even basic deep learning models such as feed forward networks. Although these studies predicted the number of mature oocytes with a relatively high performance, the employed methods lack insights into the rationale governing their predictions. This lack of interpretability poses an important limitation when applying these models in a clinical setting. Study design, size, duration This study was a retrospective analysis of a dataset of 6,430 ovarian stimulation cycles performed at a single center in the USA, aimed at building a deep learning based, interpretable model to predict the number of mature oocytes retrieved after ovarian hyperstimulation. Participants/materials, setting, methods An interpretable deep learning model was developed to predict the number of mature oocytes retrieved. The model was evaluated on an out-of-sample test set and compared to baseline models used in prior studies such as XGBoost and Linear Regression. Main results and the role of chance The model successfully predicted the retrieved oocyte count, with a mean absolute error (MAE) deviation of ± 3.14 oocytes from the ground truth. The model outperformed the performance of models used in previous studies such as XGBoost and Linear Regression which achieved a MAE of ± 3.16 and ±3.2 oocytes respectively on the test dataset. Parameter significance was measured by calculating how much parameters aided in the final prediction. The parameters found to be most significant in predicting the number of retrieved mature oocytes were (in order): total number of follicles, number of follicles size 12mm-15mm and number of follicles size 16-17mm. Parameters that were found to be of lower significance but still indicative were follicles larger than 18 mm and estradiol on trigger day. Surprisingly, age and basal FSH were of little significance. Most notably, the number of follicles in the 12mm -15mm bin were much more indicative than larger follicles of size 16-17mm and much more than follicles larger than 18mm. Furthermore, the model generates a transparent breakdown of the prediction for each patient, including a heat map and bar graph highlighting the factors influencing the decision making process at every step, creating a completely transparent prediction process. Limitations, reasons for caution The model was developed on training data from a single clinic, therefore requiring further data collection from diverse global locations to ensure the model’s ability to generalize. Wider implications of the findings This study demonstrates better oocyte prediction accuracy and transparency in the prediction process, surpassing previous models while also building trust. These advancements not only improve the safety of Artificial Intelligence tools for optimizing stimulation protocols but also contribute to reducing regulatory concerns surrounding their deployment in clinical settings. Trial registration number not applicable

Resource and animal use implications of the proposed REACH information requirements for endocrine disruptor assessment

Revised information requirements for endocrine disruptor (ED) assessment of chemicals under the European Union's Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation have been proposed. Implementation will substantially increase demands for new data to inform ED assessment. This article evaluates the potential animal use and financial resource associated with two proposed ED policy options, and highlights areas where further clarification is warranted. This evaluation demonstrates that studies potentially conducted to meet the proposed requirements could use tens of millions of animals, and that the approach is unlikely to be feasible in practice. Given the challenges with implementing either policy option and the need to minimise the reliance on animal testing, further consideration and clarification is needed on several aspects prior to implementation of the requirements. This includes how testing will be prioritised in a proportionate approach; how to harness new approach methodologies to waive higher-tier animal testing; and need for provision of clear guidance particularly in applying weight-of-evidence approaches. There is now a clear opportunity for the European Commission to lead the way in developing a robust and transparent ED assessment process for industrial chemicals which fully implements replacement, refinement, and reduction of the use of animals (the 3Rs).

Ethical Issues in Artificial Intelligence Adoption in African Higher Education Institutions in Nigeria

Purpose: The aim of the study was to investigate the ethical issues in artificial intelligence adoption in African higher education institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Ethical concerns surrounding the adoption of Artificial Intelligence (AI) in African higher education institutions reveal key findings from recent studies. These include issues of data privacy, transparency in AI algorithms, and ethical implications in decision-making processes. Universities in Africa implementing AI technologies must navigate challenges such as ensuring ethical data handling and maintaining transparency in AI systems. Unique Contribution to Theory, Practice and Policy: Ethical frameworks in AI adoption, critical theory & ethics of care may be used to anchor future studies on the ethical issues in artificial intelligence adoption in African higher education institutions. Embed ethics education into AI courses and training programs across African universities. Equip students, educators, and AI developers with the knowledge and skills to identify, analyze, and address ethical dilemmas associated with AI technologies. Develop and enforce clear, context-specific AI ethics policies and regulatory frameworks at national and institutional levels. These policies should outline guidelines for data management, algorithmic transparency, and ethical review processes.

Administrative Management, Internal Control and Organizational Culture in a Public Hospital Institution in San Juan de Lurigancho, 2023

Introduction: In the 2023 study, we sought to determine the relationship between administrative management (GA), internal control, and organizational culture in the health staff of a public hospital in San Juan de Lurigancho. It was theoretically grounded in the Theory of Human Relations and the theory of organizations as social systems. Methods: A non-experimental basic research study was carried out with a quantitative approach, using a cross-sectional correlational descriptive design. The sample included 278 healthcare workers from a population of 1000, to whom surveys validated by expert judgement were applied. Results: It was found that 61.2% of the staff perceived a regular level in GA, while 30.9% considered it efficient and 7.9% inefficient. Regarding internal control, 57.9% perceived a medium level, followed by 34.9% who considered it high and 7.2% low. Regarding organizational culture, 59.0% perceived it at a medium level, 34.5% considered it high and 6.5% low. Conclusions: It was concluded that administrative management is influenced by several factors, and the importance of maintaining a solid administrative planning, based on the mission and vision of the organization, as well as promoting transparent processes for institutional growth, was highlighted.

Unveiling the Enigma: Empowering Citizen Engagement in Decision-Making Deciphering Albania's Case

This article aims to fill the gap and analyze citizen involvement in developing and transitional democracy, focusing specifically on Albania, a country in the Western Balkans. The objective of this article is to examine the models, trends, obstacles and opportunities offered by citizen participation, as well as to evaluate the determinant factors and their impact on the transformative processes of democratization and the fragile development of Albania. The article explores the participation of citizens in the decision-making process, and their role in policy-making and democratic decision-making and tries to understand how citizens can be more actively involved in the decision-making process and how this involvement can increase the legitimacy and effectiveness of policies. In this study, we use qualitative and quantitative methods to analyze existing practices of citizen involvement in the decision-making process at different levels of local and central government. We focus on the tools and mechanisms that can be used to encourage the involvement of citizens in the decision-making process. The results of this study aim to provide recommendations for policymakers and practitioners at different levels of government to improve the involvement of citizens in the process of decision-making. In conclusion, through these recommendations, we aim to help increase transparency, responsibility and legitimacy of decision-making processes and create an environment where citizens feel involved and heard in decision-making that affects life. Received: 8 March 2024 / Accepted: 13 June 2024 / Published: 02 July 2024

The impact of Kahramanmaraş (2023) earthquakes: A comparative case study for Adıyaman and Malatya

This study examines the effects of two major earthquakes of magnitude 7.7 and 7.6 that struck Kahramanmaraş on February 6th, 2023, followed by a magnitude 6.4 quake in Hatay on February 20th, which caused major damage in 11 Turkish provinces. The study focuses on Adıyaman and Malatya and uses an interdisciplinary approach to analyze the economic and environmental impacts. Primary data sources, including field visits and interviews, reveal clear labor-related challenges in both provinces, characterized by a government-induced labor shortage. In both provinces, physical capital has been severely damaged, particularly affecting small businesses, historic bazaars, and old industrial areas. The impact on businesses varies by size and location, with Adıyaman suffering more severe setbacks than other cities. The shortage of skilled labor related to the earthquake damage affects the quality of production, which can have a serious economic impact. Transportation disruptions continue to hamper supply chains and affect companies' ability to meet their export commitments. The environmental consequences, particularly the large amount of debris, pose a major challenge. The lack of a comprehensive disaster waste plan at the central government level leads to inadequate waste management. The study recommends sorting the debris at temporary sites to obtain reusable items while paying attention to the sustainability and transparency of debris management processes. In summary, this comparative case study highlights the need for tailored approaches to address the different impacts in the 11 provinces. A one-size-fits-all solution is insufficient and an individual needs assessment is needed for each province in order to implement targeted economic and environmental recovery measures.

The state of Continuing Professional Development in East and Southeast Asia among the medical practitioners

Introduction: Continuing medical education and continuing professional development activities (CME/CPD) improve the practice of medical practitioners and allowing them to deliver quality clinical care. However, the systems that oversee CME/CPD as well as the processes around design, delivery, and accreditation vary widely across countries. This study explores the state of CME/CPD in the East and South East Asian region from the perspective of medical practitioners, and makes recommendations for improvement. Methods: A multi-centre study was conducted across five institutions in Hong Kong, Indonesia, Malaysia, Singapore and Taiwan. The study instrument was a 28-item (27 five-point Likert scale and 1 open-ended items) validated questionnaire that focused on perceptions of the current content, processes and gaps in CME/CPD and further contextualised by educational experts from each participating site. Descriptive analysis was undertaken for quantitative data while the data from open-ended item was categorised into similar categories. Results: A total of 867 medical practitioners participated in the study. For perceptions on current CME/CPD programme, 75.34% to 88.00% of respondents agreed that CME/CPD increased their skills and competence in providing quality clinical care. For the domain on pharmaceutical industry-supported CME/CPD, the issue of commercial influence was apparent with only 30.24%-56.92% of respondents believing that the CME/CPD in their institution was free from commercial bias. Key areas for improvement for future CME/CPD included 1) content and mode of delivery, 2) independence and funding, 3) administration, 4) location and accessibility and 5) policy and collaboration. Conclusion: Accessible, practice-relevant content using diverse learning modalities offered by unbiased content providers and subject to transparent and rigorous accreditation processes with minimal administrative hassle are the main considerations for CME/CPD participants. Keywords: Medical Education, Health Profession Education, Continuing Professional Development, Continuing Medical Education, Accreditation

Making a case for playful assessment as asset-based assessment through conjecture mapping

The paper proposes a theoretical design framework for playful assessment, emphasizing learner agency, engagement, and creativity. It argues that assessment should be a continuous, inclusive, transparent, and collaborative process, aligned with principles of equitable learning. The framework introduces “The Four Freedoms of Play” as heuristics for playful assessment, emphasizing aspects like the freedom to experiment, the freedom to fail, the freedom to try on identities, and the freedom of effort. These freedoms align with current educational research findings and the importance of learners' agency, identity safety, and sense of belonging. In summary, playful stances in assessment can expand what assessment is by creating flexible and creative assessment opportunities that center learners’ agency and identity, ultimately leading to an asset-based learning culture.

Effect of Corporate Governance on Financial Performance in China

Purpose: The aim of the study was to assess the effect of corporate governance on financial performance in China. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated a positive correlation between strong corporate governance practices and improved financial outcomes for companies. These practices typically include transparent decision-making processes, effective board oversight, and alignment of management incentives with long-term shareholder value. Study suggests that companies with robust corporate governance structures tend to experience higher profitability, better risk management, increased investor confidence, and enhanced access to capital. Additionally, strong corporate governance is associated with lower instances of financial misconduct and improved overall corporate reputation, which can further contribute to sustainable financial performance over time. Implications to Theory, Practice and Policy: Agency theory, stewardship theory and resource dependency theory may be used to anchor future studies on assessing the effect of corporate governance on financial performance in China. Implement best practices in corporate governance that align with empirical findings and theoretical insights to optimize financial performance. Advocate for regulatory frameworks and policies that incentivize effective corporate governance practices and align them with desired financial performance outcomes.

Designing Claim Systems in Health Insurance Companies with Microservices and Event-Driven Architecture Approach

Through digital transformation, insurance companies, especially in the health sector, are increasingly adopting modern technologies to enhance efficiency and service quality. Health insurance allows individuals or families to mitigate the financial risks associated with high and unexpected medical expenses. One crucial area is insurance claim, where a fast and accurate process is key to customer satisfaction. This study proposes the design and architecture of an insurance claim system using a microservices and event-driven approach. This approach enables insurance companies to break down applications into separate components, facilitating scalability, flexibility, and easier maintenance. Additionally, with an event-driven approach, the system can quickly respond to changes and events in the business environment. A comprehensive analysis shows that implementing microservices and event-driven architecture in the insurance claim system can enhance overall system performance, scalability, and resilience. For insurance companies, adopting microservices and event-driven architecture can lead to increased operational efficiency, reduced time to market for new products, and improved customer experiences through faster claim processing. Policyholders will benefit from quicker claim resolutions and a more transparent and responsive claim process. This study provides valuable insights for health insurance companies looking to upgrade their IT infrastructure to meet future challenges. The findings from this research will be documented to support the development of insurance business technology, specifically for health insurance claims in Indonesia.

INFLUENCE OF SAVINGS AND CREDIT COOPERATIVES SERVICES ON THE SOCIO-ECONOMIC DEVELOPMENT OF THE BENEFICIARIES A CASE OFZIGAMA CREDIT AND SAVING SOCIETY

Background:Savings and Credit Cooperatives (SACCOs) play a pivotal role in socio-economic development, particularly in developing countries. They provide financial services to their members, fostering economic growth and reducing poverty. However, the extent of their impact on the socio-economic development of their beneficiaries remains under-explored. This research aims to investigate the influence of SACCOs, with a specific focus on the Zigama Credit and Saving Society, on the socio-economic development of their beneficiaries from 2018 to 2023.The researchwas guided by three specific objectives: To assess the impact of Saving Services provided by Zigama Credit and Saving Society on the financial well-being of its beneficiaries. To examine the influence of Credit Services offered by Zigama Credit and Saving Society on the socio-economic development of its beneficiaries. And to find out the effectiveness of Training programs conducted by Zigama Credit and Saving Society in enhancing the financial literacy and skills of its beneficiaries. This research considered three theories in its theoretical framework in particular and these include Cooperative theory, Financial Inclusion Theory and Social capital theory. Methods and Materials:For this study, a descriptive research designs were employed, and data were collected by using both quantitative and qualitative methods, using interview guides and questionnaires.A sample size of 398 individuals was selected from the broader staff population of 77,010members, beneficiaries and staff of Zigama CSS, following Taro Yamanes formula for sample size determination.The collected data were analysed through statistical analysis, encompassing descriptive measures such as the mean and standard deviation to provide an overview of the data. Additionally, inferential statistical techniques were be used, focusing on the application of the Pearson correlation coefficient (r) and multiple linear regression analysis. Results: The research findings revealed that Beneficiaries perceive ZigamaCSSs saving services positively, with high satisfaction regarding interest rates (Mean=4.44, Std=0.78), accessibility (Mean=4.47, Std=0.75), and incentives for saving (Mean=4.49, Std=0.73). The societys credit services are highly regarded, with strong agreement on loan accessibility (Mean=4.38, Std=0.73), reasonable interest rates (Mean=4.35, Std=0.79), flexible loan repayment terms (Mean=4.41, Std=0.71), transparent credit approval process (Mean=4.37, Std=0.75), and positive impact on socio-economic status (Mean=4.37, Std=0.77). Training programs offered by Zigama CSS are effective, as indicated by high satisfaction with curriculum content (Mean=4.38, Std=0.73), delivery effectiveness (Mean=4.51, Std=0.69), active participation (Mean=4.22, Std=0.82), improved financial decision-making skills (Mean=4.56, Std=0.66), and strong recommendation to others (Mean=4.63, Std=0.62). There is a very strong positive correlation between SACCO services and the socio-economic development of beneficiaries (r = 0.937, p < .001), indicating that nearly 94% of the variance in beneficiaries socio-economic development can be explained by SACCO services. In conclusion, Zigama CSSs services significantly contribute to the socio-economic development of its beneficiaries, highlighting the importance of SACCO services in enhancing financial inclusion and well-being. Conclusion: Furthermore, the research made recommendations including the fact that SACCOs should prioritize customer satisfaction, expand financial education programs, ensure a supportive regulatory environment, and increase awareness about SACCO benefits. And also suggested areas that Further research could improve including comparative studies between SACCOs, longitudinal studies on SACCO impacts, qualitative studies on SACCO effectiveness, analyses of COVID-19 impacts, policy analyses, gender analyses, studies on technology adoption, and studies on SACCO impacts in rural communities.