Iterative Feedback Research Articles

The impact and importance of serious games in maritime education: an empirical application

ABSTRACT Gamification and serious gaming have emerged as alternative approaches in learning and decision-making across various domains. In maritime education and the inland waterways sector, where traditional methods may fall short in engaging learners effectively, the development of tailored serious games presents a unique opportunity. This research assesses the educational value of a serious game designed for the maritime industry and inland waterways. The game was developed through a systematic process involving a literature review, stakeholder consultations, and expert input. The methodology included iterative design, user testing, and feedback collection from participants across various sectors. The game features tasks simulating port operations, equipment handling, and inland navigation, followed by quizzes to reinforce learning. Data analysis was performed to evaluate user engagement, knowledge retention, and satisfaction levels. Evaluation results indicate high user engagement, improved knowledge retention, and participant satisfaction. This research underlines the potential of serious games in maritime education, offering innovative solutions to bridge the gap between theoretical knowledge and practical application, thereby maximizing learning outcomes and preparing individuals comprehensively for the challenges of the maritime industry and inland waterways sector.

Human-in-the-loop active learning for goal-oriented molecule generation

Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules.Scientific contributionWe present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

Rancang Bangun Aplikasi Sistem Informasi Desa Berbasis Web di Desa Kalimati

This study focuses on designing and developing a web-based information system for Kalimati Village using the Rapid Application Development (RAD) method. Data collection was conducted through interviews and field observations to identify system requirements. The design process employed Unified Modeling Language (UML) to model system workflows and structures, including use case and activity diagrams. The system, built with the Laravel 10 framework, features village profiles, population data, news, galleries, MSMEs, and APBDesa management. Using the RAD method enabled rapid development with iterative feedback from stakeholders, ensuring the system met village needs. This web-based platform improves data management efficiency, enhances transparency, and provides easy digital access to essential information for residents. The implementation of this system supports modern, effective, and transparent village governance.

Research on Guided Performance Evaluation System Based on Dynamic Information Update and Iterative Feedback

In the field of higher education, traditional performance evaluation systems often neglect the consideration of individual student differences and the provision of dynamic feedback, resulting in evaluation results that do not fully reflect the development potential and actual performance of students. This paper proposes an innovative guided performance evaluation mechanism that incorporates two core algorithms: online information updating and secondary assessment feedback. The former ensures the timeliness and accuracy of the evaluation results through real-time monitoring and updating of students' learning status, while the latter realizes the expansion of the evaluation subject and the improvement of the feedback mechanism through the interaction between tutors and students. Then, this paper adopts the hierarchical analysis method and the fuzzy comprehensive evaluation method to conduct empirical research, and the results show that the proposed guided performance evaluation mechanism can effectively improve students' satisfaction with the performance evaluation system and promote the enhancement of their independent learning ability.

Developing a framework for business analysts to bridge stakeholder and technical team gaps: Enhancing collaboration and project success

In today’s dynamic business environment, projects often fail due to misalignment between stakeholders’ needs and technical implementation. Business Analysts (BAs) play a crucial role in bridging the gap between business stakeholders, who focus on strategic objectives, and technical teams, who emphasize implementation details. However, without a structured approach, BAs may struggle to facilitate effective communication and collaboration. Developing a robust framework for BAs can significantly enhance project outcomes by ensuring clear alignment between stakeholder expectations and technical deliverables. A critical component of this framework involves stakeholder identification and analysis. BAs need to clearly understand the interests, expectations, and influence levels of different stakeholders to prioritize requirements effectively. By utilizing tools such as stakeholder matrices and engagement plans, BAs can foster trust and manage expectations, ensuring that business objectives are translated into actionable technical specifications. Effective requirements gathering and documentation is another key element. Techniques such as workshops, interviews, and process mapping can be leveraged to capture detailed business needs. Translating these into clear, structured documentation such as user stories and process flow diagrams ensures technical teams have a precise understanding of project goals, reducing the likelihood of misinterpretation. The use of collaborative tools is essential for enhancing communication between stakeholders and technical teams. Platforms like JIRA and Confluence, along with Agile methodologies, promote iterative feedback loops, ensuring that both parties remain aligned throughout the project lifecycle. Regular check-ins and sprint reviews enable stakeholders to provide real-time feedback, allowing technical teams to adjust solutions to evolving business needs promptly. Additionally, continuous training and upskilling of BAs is vital for the framework’s success. As projects become more complex, BAs must be proficient not only in communication and documentation but also in leveraging data analytics and emerging technologies to drive insights. By developing a comprehensive framework that encompasses stakeholder engagement, clear documentation, and collaborative practices, organizations can optimize project success rates, streamline workflows, and foster a culture of continuous improvement. Ultimately, the role of BAs is becoming increasingly strategic as organizations strive to remain agile and responsive in a fast-evolving market landscape. Keywords: Business Analysts, Agile Methodologies Framework, Review.

Unleashing Human Potential: A Framework for Augmenting Co-Creation with Generative AI

This paper redefines the traditional view of automation as a threat to human labor, advocating for human-AI co-creation as a strategic imperative for organizations. We propose a comprehensive six-stage framework for co-creation, emphasizing iterative feedback loops and continuous improvement to integrate generative AI into workflows effectively. Drawing from a multi-disciplinary perspective, we explore critical enablers of successful human-AI collaboration, including user-centered interface design, explainable AI systems, and fostering a culture of trust and experimentation. Real-world case studies, such as AI-enhanced visual design and creative writing, illustrate the transformative potential of co-creation across various sectors. We also propose a multifaceted measurement framework encompassing quantitative metrics (e.g., productivity gains, time-to-market acceleration) and qualitative indicators (e.g., employee well-being, skill development) to assess the impact of co-creation comprehensively. This research offers a strategic roadmap for organizations to embrace generative AI as a tool for collaboration and augmentation, thereby unlocking new levels of creativity, productivity, and employee empowerment.

Framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households

This review outlines a framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households. In response to growing environmental concerns and the need for sustainable energy solutions, this framework is designed to enhance residential solar adoption rates by customizing communication strategies to diverse consumer profiles. By leveraging consumer behavior insights, the framework addresses critical factors such as individual perceptions, social influences, financial considerations, and technological barriers that affect consumers' solar adoption decisions. The proposed framework emphasizes understanding various consumer segments, including early adopters, environmentally conscious individuals, and budget-focused households, to design targeted communication strategies. It integrates key elements like demographic analysis, psychographic profiling, and attitudinal segmentation to build a comprehensive picture of potential solar consumers. Using these insights, the framework proposes tailored messaging that resonates with each segment’s unique motivations and concerns, such as energy cost savings, environmental impact, and long-term investment value. To maximize engagement, the framework recommends deploying these tailored messages across a mix of digital and traditional media channels, ensuring optimal reach and relevance. Social media, for instance, can be effectively used to reach environmentally conscious and tech-savvy segments, while community events and local partnerships may better engage older or budget-focused demographics. Moreover, the framework advocates for the use of testimonials, data visualization, and interactive content to enhance credibility and user experience, further breaking down perceived barriers such as installation costs, maintenance concerns, and technology reliability. By focusing on consumer-centric communication, this framework aims to increase awareness, interest, and trust in solar energy solutions, thereby fostering a supportive environment for adoption. It also highlights the importance of iterative feedback mechanisms to continuously refine strategies and address emerging consumer needs. Ultimately, this framework serves as a strategic guide for policymakers, marketers, and solar companies to drive meaningful consumer engagement, boost conversion rates, and contribute to the broader goal of achieving sustainable energy adoption across the U.S. residential sector.

Chatbots in Cybersecurity: Enhancing Security Chatbot Efficacy through Iterative Feedback Loops and User-Centric Approaches

Chatbots are of continuous importance in our interactive lives. Although used in several domains, there are questions about its security assurance; therefore, there is a need to know its capabilities, limitations, and challenges in cybersecurity. The research explores the use of chatbots in enhancing cyber defences and their potentials. It examines chatbots’ current applications in cybersecurity, including IT services, information protection, and user education. Furthermore, the research proposes implementing an Intelligent Chatbot Security Assistant (ICSA) model on WhatsApp to detect and respond to cyberattacks based on user conversations and identifies the challenges with this implementation. To address these challenges, it suggests incorporating enhanced privacy measures, real-time monitoring, rigorous evaluation and validation, and concludes with user-centric design principles using iterative feedback. This research provides valuable insights into the use of chatbots in cybersecurity, their current level of research and implementation as a cybersecurity tool, and directions for future research.

EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study.

Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle. This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems. This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops' insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development. In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care. The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.

Cross-Cultural Biology Teaching Using Next-Generation Science Standards

This study explored Next Generation Science Standards (NGSS) in cross-cultural biology teaching through collaborative lesson studies involving educators from the USA and the Philippines. We employed grounded theory and examined iterative feedback processes during lesson development to refine learning exemplars. Learning exemplars validation affirmed their alignment with both NGSS and the Philippine science education frameworks, ensuring cultural relevance and educational rigor. Five key themes were identified as pivotal: retrieval of prior knowledge, fostering meaningful learning experiences, enhancing memory and retention, fostering active engagement, and cultivating critical thinking skills—integral for developing culturally responsive curricula. Moreover, students became independent learners, responsible for their learning, reflective and critical thinkers, problem solvers, inquiry-oriented, creative, collaborative communicators, modelers, data analysts, persistent, adaptable, and self-directed. Implications include enhancing educational policies to support cultural diversity and integrating cross-cultural learning exemplars to enhance global teaching practices. This study underscored the transformative potential of cross-cultural collaboration in advancing science education, fostering engaging learning environments, and preparing students for global citizenship.

Randomized iterative feedback tuning for fast MIMO feedback design of a mechatronic system

Iterative feedback tuning (IFT) enables the tuning of feedback controllers using only measured data to obtain the gradient of a cost criterion. The aim of this paper is to reduce the required number of experiments for MIMO IFT. It is shown that, through a randomization technique, an unbiased gradient estimate can be obtained from a single dedicated experiment, regardless of the size of the MIMO system. The gradient estimate is used in a stochastic gradient descent algorithm. The approach is experimentally validated on a mechatronic system, showing a significantly reduced number of experiments compared to standard IFT.

Cultural adaptation of an internet-based self-help app for grieving Syrian refugees in Switzerland.

Loss and grief pose significant challenges for victims of armed conflicts, such as Syrian refugees. Internet-based interventions (IBIs) present a promising solution to address this treatment gap and provide adequate support. However, research on grief, grief support, and IBIs remain largely limited to Western cultural contexts, and culturally adapted IBIs for grief are needed. Following the Reporting Cultural Adaptation in Psychological Trials (RECAPT) framework, this study aimed to develop and further adapt a culturally sensitive IBI for bereaved Syrian refugees in Switzerland. The study employed qualitative methods. Initially, formative research was conducted to create a first version of the intervention, including semi-structured interviews with 10 experts to identify necessary cultural adaptations. The preliminary version of the intervention was then presented to six potential users and three experts to gather feedback on additional cultural adaptations through two iterative feedback rounds. The first round involved semi-structured interviews using a "paper version" of the intervention, followed by a second round with a walk-through think-aloud protocol with a beta version. Data were analyzed using framework analysis. The input from various key informants at different stages of development provided valuable feedback on surface and deep structure adaptation, which may enhance treatment adherence, acceptance, and motivation. These findings provide important insights and recommendations for the cultural adaptation of interventions and may help address the treatment gap for bereaved Syrian refugees.

Development of a Human Factors Based Guideline to Support the Design, Evaluation and Continuous Improvement of Clinical Decision Support

ObjectiveTo develop a vendor-agnostic, Human Factors (HF) based guideline to guide the design, evaluation, and continuous improvement of clinical decision support (CDS). Participants and MethodsThe study utilized a two-phased iterative approach between June 2022 to June 2024. Phase 1 involved a search for relevant industry standards and literature, and consultation with multidisciplinary subject matter experts. Phase 2 involved a workshop with 30 healthcare and academic stakeholders to evaluate face validity and perceived usefulness of the initial section of the guideline. Participants were asked if the guideline met their expectations, to report on usefulness and ease of use, and to suggest areas for improvement. ResultsPhase 1 resulted in a compilation of accessible, best practice and context appropriate HF guidance for CDS design and optimization. The guideline supports users in determining whether use of CDS is appropriate, and if yes, CDS options and design guidance. During Phase 2, the guideline addressed 15 of participants’ 19 expectations for a CDS guideline. Participants said the guideline was helpful, comprehensive, easy to use, and provided step by step guidance, boundaries and transparency around CDS decisions. Participants recommended strengthening guidance around the need to understand system capabilities and the technical burden or complexity of CDS, and further guidance on how to approach CDS optimization using the guideline. ConclusionThe two phased iterative development and feedback process resulted in the development of a HF-informed guideline to provide consolidated, accessible and current best practice guidance on the appropriateness of CDS and CDS options, as well as designing, evaluating and continuously improving CDS. Future work will evaluate the impact and implementation of the guideline in real world settings.

Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis

The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.

NAMAH—An Innovative Tele-ECHO Mentoring Program to Foster Well-being Among Physicians

Background: The current study aimed to develop and implement the National Assistance in Mental Health for Health Care Providers (NAMAH) module, which focused on wellness and building resilience for a cohort of physicians. Methods: The NAMAH module is a 12-week tele-mentoring program leveraging videoconference technology that uses the ECHO (Extension of Community Healthcare Outcome) HUB and SPOKE and consists of real-life case discussions and a brief didactic. The module’s content was developed after iterative feedback from experts and incorporated suggestions from healthcare providers (HCPs) following a needs assessment. A pre and post-design was used to assess the impact of the module on psychological distress using the self-reported Kessler Psychological Distress Scale (K10) and burnout using the Maslach Burnout—Inventory-Human Services Survey (MBI-HSS) among the 32 physicians who participated. Results: There was a significant decrease in the mean scores before (19.5 ± 6.27) and after (17.38 ± 6.23) the NAMAH module ( p < .05) in the psychological distress as measured by K 10 with a Cohen’s d of 0.41 (95% CI: 0.05–0.77). There was also a significant decrease in the mean scores after the intervention in the emotional exhaustion and depersonalization domain of the MBI-HSS with a medium effect size (Cohen’s d of 0.65) and large effect size (Cohen’s d of 0.94), respectively. Conclusion: The findings from this pilot study lay a foundational framework, encouraging further exploration, research, and scaling-up of such interventions to enhance mental health among physicians and HCPs.

Design and Implementation of a Computer-Controlled Hybrid Oscillatory Ventilator.

During mechanical ventilation, lung function and gas exchange in structurally heterogeneous lungs may be improved when volume oscillations at the airway opening are applied at multiple frequencies simultaneously, a technique referred to as multifrequency oscillatory ventilation (MFOV). This is in contrast to conventional high-frequency oscillatory ventilation (HFOV), for which oscillatory volumes are applied at a single frequency. In the present study, as a means of fully realizing the potential of MFOV, we designed and tested a computer-controlled hybrid oscillatory ventilator capable of generating the flows, tidal volumes, and airway pressures required for MFOV, HFOV, conventional mechanical ventilation (CMV), as well as oscillometric measurements of respiratory impedance. The device employs an iterative spectral feedback controller to generate a wide range of oscillatory waveforms. The performance of the device meets that of commercial mechanical ventilators in volume-controlled mode. Oscillatory modes of ventilation also meet design specifications in a mechanical test lung, over frequencies from 4 to 20 Hz and mean airway pressure from 5 to 30 cmH2O. In proof-of-concept experiments, the oscillatory ventilator maintained adequate gas exchange in a porcine model of acute lung injury, using combinations of conventional and oscillatory ventilation modalities. In summary, our novel device is capable of generating a wide range of conventional and oscillatory ventilation waveforms with potential to enhance gas exchange, while simultaneously providing less injurious ventilation.

Mobile Health-Based Health Literacy Weight Management Intervention Using Smart Devices for Adolescents: A Convergent Mixed-Methods Pilot Study.

This study aimed to develop and evaluate the feasibility of a mobile health-based health literacy weight management intervention program for adolescents during the COVID-19 pandemic. Therefore, a convergent mixed-method design was used. The "GO! GO! Smart Healthy" intervention development engaged multidisciplinary healthcare professionals, utilizing mobile health applications and smartwatches. Participants provided data on weight management, dietary habits, and health literacy via an mHealth app, while smartwatches collected objective information on physical activity and sleep. The 12-week pilot study was conducted from March to September 2021. It evaluated the program's feasibility, employing both quantitative and thematic analyses of focus group discussions for a comprehensive assessment. Of the 27 participants, 44.4% were overweight or obese, with insufficient physical activity reported. Thematic analysis identified three main themes: empowerment, self-awareness and self-regulation, and self-comparison and reflection. Adolescents engaged with smartwatches, demonstrated increased awareness, altruistic behavior, and satisfaction. After integrating mixed-method data, revisions ensured the program's alignment with participants' preferences. Despite pandemic disruptions, mHealth apps ensured uninterrupted research, showcasing technology's evolving role in healthcare and education. Iterative feedback with participants ensured program suitability, fostering effectiveness. Trial Registration: ClinicalTrials.gov: NCT04759716.

Comparison of Large Language Models in Generating Machine Learning Curricula in High Schools

With the rapid advancement of artificial intelligence technologies, the integration of AI concepts into educational curricula represents an increasingly important issue. This paper presents a comparative analysis of four AI large language models, ChatGPT (now GPT-4o), Bard (now Gemini), Copilot, and Auto-GPT, in the last year, progressing from the previous into the newer versions, thus also revealing the progress over time. Tasks were selected from the Valence project, which aims to advance machine learning in high school education with material designed by human experts. The four LLMs were assessed across 13 topics, 35 units, and 12 code segments, focusing on their code generation, definition formulation, and textual task capabilities. The results were analyzed using various metrics to conduct a comprehensive evaluation. Each LLM was allowed up to five attempts to produce outputs closely aligned with human-written materials, with experts providing iterative feedback. This study evaluated the effectiveness and accuracy of these LLMs in educational content creation, offering insights into their potential roles in shaping current and future AI-centric education systems.

AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.

Measuring fidelity to manualised peer support for people with severe mental health conditions: development and psychometric evaluation of the UPSIDES fidelity scale

BackgroundPeer support workers provide support for people experiencing mental health conditions based on their own lived experience of mental health problems. Assessing fidelity to core ingredients of peer support is vital for successful implementation and intervention delivery. Modifications to its implementation are needed when scaling up to different socio-economic settings, raising further uncertainty about fidelity. As part of a large multi-centre study on peer support called Using Peer Support In Developing Empowering Mental Health Services (UPSIDES), we developed and evaluated the psychometric properties of the UPSIDES Fidelity Scale.MethodsWe constructed the fidelity scale based on an initial item pool developed through international expert consultation and iterative feedback. Scale refinement involved site-level expert consultation and translation, resulting in a service user-rated 28-item version and a peer support worker-rated 21-item version assessing receipt, engagement, enactment, competence, communication and peer support-specific components. Both versions are available in six languages: English, German, Luganda, Kiswahili, Hebrew and Gujarati. The scale was then evaluated at six study sites across five countries, with peer support workers and their clients completing their respective ratings four and eight months after initial peer support worker contact. Psychometric evaluation included analysis of internal consistency, construct validity and criterion validity.ResultsFor the 315 participants, item statistics showed a skewed distribution of fidelity values but no restriction of range. Internal consistency was adequate (range α = 0.675 to 0.969) for total scores and all subscales in both versions. Confirmatory factor analysis indicated acceptable fit of the proposed factor structure for the service user version (χ2/df = 2.746; RMSEA = 0.084) and moderate fit for the peer support worker version (χ2/df = 3.087; RMSEA = 0.093). Both versions showed significant correlations with external criteria: number of peer support sessions; perceived recovery orientation of the intervention; and severity of illness.ConclusionsThe scale demonstrates good reliability, construct and criterion validity, making it a pragmatic and psychometrically acceptable measure for assessing fidelity to a manualised peer support worker intervention. Recommendations for use, along with research and practical implications, are addressed. As validated, multi-lingual tool that adapts to diverse settings this scale is uniquely positioned for global application.Trial registrationISRCTN, ISRCTN26008944. Registered on 30 October 2019.