Successful Integration Research Articles

Migration and linguistic integration: an investigation of Chinese immigrants’ language learning experience before moving to Africa

ABSTRACT The current study investigates the linguistic integration of Chinese immigrants, with a particular focus on their language learning experiences before migrating to Africa. As China’s presence in Africa grows, understanding how Chinese migrants prepare linguistically for their new environments becomes crucial. Through a mixed-methods approach, which include surveys and interviews conducted primarily in Cameroon, the research highlights the value of language skills for successful integration and examines the potential consequences of inadequate language proficiency. The study suggests that both Chinese and African governments should collaborate to encourage Chinese immigrants to learn destination languages even before migrating to Africa. This could be achieved by promoting proactive language acquisition initiatives, reevaluating language policies, and implementing standardised language testing. Emphasising the language learning experience is crucial not only for effective communication but also for reducing stereotypes and fostering deeper understanding and integration between the Chinese expatriate community and local populations. Providing language resources and support can greatly enhance crosscultural interactions and promote the harmonious coexistence of diverse Chinese communities across Africa.

Efficient synthesis of novel phenanthroline-dimedone derivatives using Pd@HQBI-SPION as a versatile palladium-immobilized catalyst

This paper presents the synthesis and application of a novel catalyst for carbon–carbon bond formations, comprising palladium immobilized on 2-(5-hydroxyquinolin-8-yl)-1H-benzo[d]imidazole-5-carboxylic acid modified superparamagnetic iron oxide nanoparticles (Pd@HQBI-SPION). The synthesis involved the attachment of HQBI ligands to SPION using APTES, followed by the immobilization of palladium. Characterization techniques, including FTIR, TEM, and magnetic measurements, confirmed the successful synthesis and structural integrity of Pd@HQBI-SPION. The catalytic activity of Pd@HQBI-SPION was evaluated in various carbon–carbon bond formation reactions, demonstrating high efficiency and reusability. 8 different derivatives bearing several electron withdrawing and electron donating functionalities were used as starting materials and the products were obtained in high isolated yields (75–97%). The catalyst exhibited excellent performance in one-pot synthesis of phenanthroline-dimedone polycyclic derivatives via C-alkylation followed by intramolecular O-alkylation of phenanthroline with dimedone. The products are obtained in high to excellent yields is described. This protocol presents a highly selective synthetic method for the construction of polycyclic aromatic compounds containing nitrogen and oxygen atoms.

Screening and Analysis of Skin Cancer Treatment Using Biocomponents of Plants Using Backpropagation Neural Networks: A Comprehensive Review

: In recent years, the use of natural compounds derived from plants for the treatment of skin cancer has gained significant attention due to their potential therapeutic effects and minimal side effects. This review focuses on the innovative approach of utilizing biocomponents sourced from plants in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting the escalating global burden of skin cancer and the limitations of conventional treatment approaches. With the rise in concerns about the adverse effects of synthetic drugs, researchers have turned their attention towards exploring the therapeutic potential of plant-derived biocomponents. These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer properties, making them suitable candidates for skin cancer treatment. One of the key challenges in harnessing the potential of plant-derived compounds is the need for accurate screening and analysis of their effects. This is where backpropagation neural networks, a type of artificial neural network, comes into play. These networks can process complex data and recognize intricate patterns, enabling them to predict the efficacy of various biocomponents in combating skin cancer. The review delves into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore, the review explores several case studies that demonstrate the successful integration of plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps involved in training the neural network using relevant datasets and optimizing its performance for accurate predictions. While the integration of plant-derived compounds and BPNN shows great promise, the review also addresses the existing challenges and limitations. These include the need for comprehensive and standardized datasets, potential biases in training data, and the complexity of neural network architectures. The regulatory considerations surrounding plant-based therapies are also discussed, highlighting the importance of rigorous testing and validation.

Artificial intelligence applications in cataract and refractive surgeries.

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field. Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring. The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

Academic nursing administrators perceptions of replacing clinical hours with simulation

BackgroundNursing education has seen a shift towards simulation-based education (SBE) to meet the demands of a rapidly evolving healthcare landscape. Maryland's Clinical Simulation Resource Consortium (MCSRC) aimed to enhance SBE utilization; however, noted a decline in SBE usage post pandemic prompting an investigation into nurse administrators' perspectives on replacing clinical hours with SBE. MethodsThis descriptive quality improvement study was informed by Rogers' Diffusion of Innovation (DOI) Theory. Semi-structured interviews were conducted with four academic nurse administrators from various pre-licensure nursing programs in Maryland. The data collected were analyzed using thematic analysis. ResultsNurse administrators who value SBE as a safe learning space are integral when building and maintaining infrastructure to support simulation usage. Identified barriers related to faculty buy-in, workload, and inadequate human resources to deliver high-quality simulations can hinder sustained adoption of clinical simulation. ConclusionsThis study highlights the necessity of continuous faculty training in SBE and dedicated resources to support simulation infrastructure. Recommendations include sustained investment in faculty development, creating specialized roles for simulation champions, and fostering collaborations to strengthen simulation integration. The findings emphasize the pivotal role of nurse administrators in navigating challenges and maximizing SBE's potential to replace clinical hours in nursing education, urging ongoing support and strategic planning for successful integration.

Comparing the dental knowledge of large language models.

Introduction With the advancement of artificial intelligence, large language models (LLMs) have emerged as technology that can generate human-like text across various domains. They hold vast potential in the dental field, able to be integrated into clinical dentistry, administrative dentistry, and for student and patient education. However, the successful integration of LLMs into dentistry is reliant on the dental knowledge of the models used, as inaccuracies can lead to significant risks in patient care and education.Aims We are the first to compare different LLMs on their dental knowledge through testing the accuracy of different model responses to Integrated National Board Dental Examination (INBDE) questions.Methods We include closed-source and open-source models and analysed responses to both 'patient box' style board questions and more traditional, textual-based, multiple-choice questions.Results For the entire INBDE question bank, ChatGPT-4 had the highest dental knowledge, with an accuracy of 75.88%, followed by Claude-2.1 with 66.38% and then Mistral-Medium at 54.77%. There was a statistically significant difference in performance across all models.Conclusion Our results highlight the high potential of LLM integration into the dental field, the importance of which LLM is chosen when developing new technologies, and the limitations that must be overcome before unsupervised clinical integration can be adopted.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

The religious legacy of śiwa-buddha at Kalibukbuk temple, Buleleng: A historical and religious approach

Background: This study explores the religious legacy of Śiwa-Buddha at Kalibukbuk Temple in Buleleng, aiming to highlight the historical and religious aspects that contribute to its significance. The integration of Śiwa and Buddha traditions in Balinese religious culture reflects the island’s parallel religious landscape, particularly during the Majapahit era. Previous studies have discussed the blending of Śiwa and Buddha elements in various Balinese temples, yet there is limited focused research on Kalibukbuk Temple. This study addresses this gap by analyzing historical records and religious texts, shedding light on how the Śiwa-Buddha harmonization shaped local religious practices. Methods: The research utilizes a qualitative method with a historical-analytical approach, combining literature review, field observations, and interviews with religious practitioners. Data collection involved an examination of temple inscriptions, carvings, and architecture to interpret symbolic elements related to Śiwa-Buddha teachings. A comparative analysis with similar temples in Bali was also conducted to emphasize common patterns and distinctive characteristics. Findings: The findings indicate that Kalibukbuk Temple serves as a cultural and spiritual focal point in Buleleng, with Śiwa-Buddha elements prominently reflected in its architectural design, deity iconography, and ritual practices. The temple’s inscriptions reveal a deliberate effort to harmonize Śaivism and Buddhism, which suggests an inclusive religious policy of the era. Furthermore, the study shows that this religious harmonization at Kalibukbuk facilitated greater social cohesion within the local community by bridging religious differences. Conclusion: In conclusion, the study reveals that the Kalibukbuk Temple's religious legacy illustrates a successful integration of Śiwa-Buddha traditions, reflecting Bali’s broader harmonized religious identity. Novelty/Originality of this article: The novelty of this research lies in its focused examination of a lesser-studied temple, providing fresh insights into Balinese religious history. This study contributes to the understanding of how religious harmonization played a vital role in shaping cultural and religious continuity in Bali.

Analytic modeling and validation of strain in textile-based OLEDs for advanced textile display technologies

In the IoT era, the demand for wearable displays is rapidly growing, catalyzing the advancement of research into textile-based organic light-emitting diodes (OLEDs). This growing interest stems particularly from the inherent flexibility of textile-based OLEDs1,2, allowing for seamless integration into the dynamic and interactive functionalities of cutting-edge wearable technology, alongside their superior electrical performance. The durability and mechanical robustness of these displays, especially under physical stress and deformation, are critical to their practical application and longevity. Thus, understanding and enhancing the mechanical properties of textile-based OLEDs is paramount for their successful integration into wearable technologies. However, many studies assessing the mechanical properties of OLEDs have predominantly relied on simplistic bending test outcomes determined by the radius, often neglecting or insufficiently analyzing the strain exerted on the OLEDs atop textile substrates in relation to curvature of these devices. Existing analyses typically presume pure bending, though such an assumption leads to considerable errors in strain estimations, making such approaches problematic if the goal is practical application in actual wearable display products. To address these limitations, an analytic model that includes a comprehensive energy equation is introduced, considering the stretching energy, bending energy, and shear energy of each layer composing the textile substrate. This holistic approach provides a novel formula specifically designed to calculate the top surface strain of textile substrates. Robust validation of this formula is conducted by comparing its results with strain measurements obtained from digital image correlation (DIC) and finite element analysis (FEA) outcomes from ANSYS across various bending radii (or equivalently, curvatures). The close alignment of the calculated strain values with those derived from DIC and FEA not only underscores the precision of this formula but also highlights its significant potential for enhancing the designs and functionalities of future wearable display technologies under real-world conditions.

Examining the factors influencing sustainability performance in the UK food industry

ABSTRACT This research examines the role of organisational culture (OC) and supply chain integration (SCI) in improving sustainable supply chain performance (SSCP) in the food manufacturing industry in the UK. A competing value framework (CVF) was used to categorise OC into developmental, group, rational and hierarchical cultures. Qualitative interviews with 11 senior managers in the UK food industry were conducted and manually analysed. We found that three competing values in developmental, group and hierarchical cultures are effective in implementing and achieving higher SSCP. The values inherent in these cultures also trigger internal and external integration through which the firms acquire sustainability ideas, knowledge, training and skills from customers and suppliers to improve SSCP. We also found that within the context of sustainability, internal integration is critical to a successful external integration. The study contributes immensely to sustainability literature by highlighting the actual OC values relevant to achieving higher SSCP and establishing SCI’s mediating role. The study further assesses how SCI is established and formed by firms on the verge of achieving higher SSCP. Managers are advised to adopt blended competing values (developmental, group and hierarchical culture) and strengthen and heighten systemic SCI to implement sustainability practices, improve SSCP as well as overcome sustainability challenges.

Exploring AI-driven solutions for Libyan agriculture: Current applications and strategic pathways

This paper aims to investigate the usability of AI in the agriculture sector for Libyan business development. The study employs an online survey with an interview with an expert to gather insights from participants operating in the agriculture sector in Libya. The survey comprises a set of questions covering various aspects related to AI adoption in agriculture business development. The collected data helped to assess the current utilization of AI technologies, identify potential areas of improvement, and explore the perceptions and expectations regarding AI's future role in Libyan agriculture. The findings provide a valuable general insight into the current state of AI adoption in agriculture sector in Libya, highlighting both the progress made and the areas where further efforts are needed to fully leverage the potential of AI technologies for sustainable and efficient agricultural practices. The finding indicated that addressing challenges related to infrastructure, data availability, and capacity building will be crucial in realizing the full potential of AI in Libyan agriculture. Collaborative efforts between stakeholders and supportive policy frameworks are essential for successful AI integration and the transformation of agricultural practices in Libya. These results help to support policymakers, businesses, and stakeholders interested in leveraging AI to enhance agricultural practices and promote sustainable business development in Libya.

Autonomous and Electric Vehicles in Urban Living Labs: Smart Mobility Strategies for the Future

The deployment of electric, connected, and autonomous vehicles on public roads presents a significant challenge that can be addressed through previously established frameworks developed globally for implementing these technologies as part of an urban living lab (ULL). This systematic review, based on records from four distinct databases, focuses on projects that have conducted deployments of self-driving technologies in streets within urban environments. The review describes relevant information about various initiatives, including a classification of the stages of development reached according to the urban area covered, safety considerations, and lessons learned for optimal deployment. On-board sensing technology, digital infrastructure, and energy and communication systems emerge as the essential components of a ULL with autonomous vehicles (AVs). A crucial goal for smart cities is ensuring the scalability of large-scale deployments of such ULLs for safe, clean, and future mobility experimentation. This can only be achieved through effective coordination among academia, government, industry, and society to guarantee the successful integration of multiple projects in a unique environment.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium

IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

Hybrid and Electric Vehicle (EV) integration in public transport: Challenges and opportunities

As cities worldwide grapple with the dual challenges of increasing urbanization and climate change, the integration of hybrid and electric vehicles (EVs) into public transport systems emerges as a critical solution. This article examines the multifaceted challenges and opportunities associated with adopting hybrid and EV technologies for buses and coaches. Key challenges include the high initial costs, the need for robust infrastructure, and concerns regarding vehicle reliability. Conversely, the transition offers significant benefits, such as substantial emissions reductions, improved operational efficiency, and enhanced public perception of sustainable practices. Through an evaluation of successful case studies from cities like London, Amsterdam, and Shenzhen, this article highlights effective strategies and outcomes achieved in the realm of public transport electrification. Ultimately, the findings underscore the necessity for collaborative efforts among stakeholders to drive the successful integration of hybrid and EV technologies, paving the way for a more sustainable urban mobility future.

Staff shortages in hospitality and tourism industries: how technology influences?

PurposeThis paper offers an introduction and sets the stage for the theme issue. It describes the methodology used and highlights the articles that address the strategic question of how hospitality and tourism industries can minimize the effects of labor shortages by embracing advanced technologies.Design/methodology/approachThis special issue explores the potential of automation, artificial intelligence, robotics and advanced data analytics in mitigating staff shortages. Automation streamlines operations, AI enhances customer interactions, robotics reimagines service delivery and data analytics informs strategic decisions. The study addresses a strategic question by combining literature reviews, data analysis, expert insights from practitioners and practical solution recommendations.FindingsThe theme issue presents research that explores how advanced technologies can be effectively utilized to mitigate labor shortages in the hospitality and tourism industries. Automation streamlines operations, AI enhances customer interactions, robotics reimagines service delivery and data analytics informs strategic decisions. However, successful integration of technology requires careful planning, training and ongoing maintenance to ensure a balance between technology and human interaction. By embracing these technologies, businesses can better cope with staffing deficits and position themselves for long-term success in a rapidly evolving environment.Originality/valueThis special issue explores the ways in which advanced technologies, such as automation, AI and robotics, can be leveraged to alleviate the labor shortage crisis faced by the hospitality and tourism industries worldwide. From developed to developing countries, these industries are grappling with severe labor shortages, particularly in terms of skilled workers. The articles presented in this issue offer valuable insights and strategies for addressing this pressing issue.

Enhanced Photodegradation of Congo Red Using RSM‐Optimized TiO₂ on Borassus flabellifer Male Inflorescence Biochar

AbstractThis study explores the use of biochar, derived from Borassus flabellifer male inflorescence biomass as an alternative support for titanium dioxide (TiO2) to enhance its photocatalytic activity in degrading Congo red (CR) dye pollutant. The biochar was synthesized through controlled pyrolysis and subsequently combined with TiO2 to form a nanocomposite via hydrothermal method. The primary goal of creating this nanocomposite is to breakdown azo dyes, such as CR, using UV irradiation. Comprehensive characterization techniques, including XRD, FTIR, SEM coupled with EDAX, XPS, and UV‐DRS confirmed the successful integration of biochar with TiO2. The enhanced photocatalyst performance is attributed to the increased surface area, improved light absorption, and efficient charge separation facilitated by the biochar support. Furthermore, the experimental parameters were optimized using the response surface methodology (RSM). These studies demonstrated that the CR concentration, contact duration, pH, and photocatalyst quantity are critical factors in the photocatalytic oxidation process. The strong correlation coefficient for the first order model indicated that the data described by the RSM, agreed with the obtained results. This study highlights the potential of agricultural waste‐derived biochar as a cost‐effective and sustainable support for TiO₂, offering a viable solution for the remediation of dye‐contaminated water.

Exploring E-Commerce Live-Streaming Strategy in China

This article conducts a comprehensive analysis of the most cutting-edge topics in live-streaming e-commerce by reviewing 15 studies published between 2019 and 2024. The research is organized around four core areas of inquiry: live-streaming e-commerce modes and strategies, user participation and behavior, brand marketing and platform strategy, and technology application and influence. The review demonstrates the influence of diverse sales modes, including self-operation and third-party live streaming, on market competition and consumer engagement. Furthermore, the analysis identifies the influence of several key factors on consumer purchasing behavior, the strategic approaches adopted by manufacturers in integrating live-streaming channels, and the implications of AI and blockchain technologies in live-streaming commerce. The findings emphasize the significance of context-specific strategies in optimizing live-streaming e-commerce. They suggest that the successful integration of these technologies and strategies can lead to enhanced consumer engagement, augmented brand visibility, and improved overall market performance. Nevertheless, the study also identifies potential challenges and limitations, including the unpredictability of real-world market dynamics and the risk of reduced innovation in algorithm-driven economies.

Integrating Lean Construction with Sustainable Construction: Drivers, Dilemmas and Countermeasures

Sustainable construction (SC) has become a primary objective of the building and construction industry due to the growing demand for reduced environmental impacts and more efficient resource use. Simultaneously, lean construction (LC), which focuses on minimizing waste and maximizing productivity, offers an opportunity to achieve greater sustainability in the construction process. This study explores the integration of LC and SC through a systematic literature review, employing VOSviewer software for quantitative analysis alongside qualitative analysis in conjunction with a comprehensive literature review. The findings reveal substantial synergies between LC and SC, including cost savings, waste reduction, and enhanced safety. However, challenges such as cost conflicts, environmental trade-offs, and worker welfare concerns act as barriers to effective integration. To address these challenges, this study proposes leveraging dynamic capabilities (DCs)—such as change management, organizational learning, organizational flexibility, and continuous improvement and innovation—as strategies for the successful integration of LC and SC. These insights contribute to academic research and industry practice, providing a practical framework for integrating LC and SC to improve sustainability and efficiency in construction projects.

Integrated models for concurrent disorders from a Public Mental Health lens: a Systematic Review

Abstract Concurrent disorders (COD), historically defined by the World Health Organization as the simultaneous presence of a psychoactive substance use disorder and another psychiatric disorder within the same individual, pose a significant challenge to public mental health. In today’s global landscape of mental health services, the prevalence of COD is on the rise, underscoring the need for an integrated approach to management. This review aims to provide an overview of existing global models and the evaluation approaches. This systematic review examined research evidence in PubMed, Scopus and Web of Science up to 2023 regarding integrated management systems for COD. Following PRISMA guidelines, we included articles describing specific models and evaluating the effectiveness of integrated versus non-integrated approaches. Quality assessment was conducted using the appropriate scales. Out of 7700 screened articles, 18 were included, all of fair or good quality. A diverse array of patient care models, programs, and interventions addressing comorbidities between mental health and substance use disorders emerged. The majority of studies focused on clinical outcomes, particularly alcohol and drug dependence, with global mental health consequences also under examination. Psychodiagnostics and social outcomes were also considered in a significant portion of the research, shedding light on health services utilization patterns. The integrated model for concurrent disorders in psychiatry stands as an effective approach, yet its implementation encounters challenges. This study showcases successful integration examples, providing valuable insights for policymakers and healthcare professionals to enhance existing models or develop new ones, particularly within European healthcare reforms like Italy’s restructuring of primary care, where they could serve as a criterion for the setting and organization of community houses. Key messages • COD, involving both substance use and psychiatric disorders, present a significant challenge to public mental health. Rising prevalence underscores the need for integrated management approaches. • Insights from successful integration examples offer valuable guidance for healthcare professionals and policymakers, particularly within healthcare reforms like Italy’s restructuring of primary care.

Development of a risk-stratified shared decision-making tool for colorectal cancer screening

Abstract European guidelines stress the importance of informed decision-making in colorectal cancer (CRC) screening. However, tailored tools that facilitate decision-making for underserved people and general practitioners (GPs) in Flanders are lacking. This study aimed to develop a tailored shared decision-making (SDM) tool for CRC screening, specifically a video decision aid, using a co-creative process. The development involved two rounds of focus group discussions with underserved people and five local quality group meetings with GPs in various parts of Flanders, Belgium. Qualitative data were analyzed using a Modified-Grounded Theory Analytical (M-GTA) approach to identify key themes. Findings revealed four core themes. The first theme, ‘Recognizing the Barriers to Screening’, identifies challenges such as a lack of time, staff, and tools for GPs, as well as language and literacy barriers for underserved people. The second theme, ‘Learning from Existing Tools’, highlights successful elements like visuals and simple language and limitations such as low accessibility and poor navigation. The third theme, ‘Determining the Content and Design,’ reveals that GPs seek user-friendly tools for at-home viewing, while underserved people value personalized and clear information about CRC risk factors and screening costs. The final theme, ‘Identifying Strategies for Integration,’ stresses the importance of a structured, collaborative care approach for the successful integration of the tool. The proposed modifications for the SDM tool include enhancing its accessibility with multiple language options, using simple and relatable visuals, and providing personalized risk discussions for underserved people and GPs. Nonetheless, further testing is needed to confirm the effectiveness of the SDM tool in supporting preventive talks and informed decision-making in CRC screening among these target groups. Key messages • A structured, collaborative care approach is needed for successful SDM tool integration during preventive talks with underserved people. • Further testing is needed to confirm the tool’s effectiveness in supporting informed decision-making in CRC screening for underserved people.