Individual Clients Research Articles

Bridging the Gap: Insights From Telepractice Augmentative and Alternative Communication Services in the Digital Age

Purpose: The United States Society for Augmentative and Alternative Communication and the Patient-Provider Communication Network sought to quantify the impact of utilization of and access to telepractice for the provision of augmentative and alternative communication (AAC) services. The purpose of this article is to summarize findings from two surveys that explored the kinds of AAC services delivered remotely, barriers to AAC addressed through telepractice, and impact of the availability of remote AAC services on people with communication disabilities and their caregivers. Method: The study team developed two online surveys: (a) for speech-language pathologists (SLPs) and (b) for individual AAC clients (or family members in the case of pediatric clients). Results: Survey responses were obtained from 66 SLPs and 90 AAC clients/family members. Access to telepractice allowed clients/family members to overcome both access and financial barriers. Respondents indicated that the availability of AAC telepractice services reduced transportation barriers, increased access to services by appropriately trained clinicians, and improved the outcomes for individuals needing AAC. Conclusions: Telepractice has made it possible for individuals with significant medical challenges, as well as those residing in areas lacking qualified providers, to receive services. Given that both clinicians and clients/families indicated that travel to receive services posed a significant barrier to access medically necessary treatment, the anticipated December 2024 end of Centers for Medicaid and Medicare Services' authorization of reimbursement for telepractice services will be detrimental to many needing AAC services. Supplemental Material: https://doi.org/10.23641/asha.27885447

Trust driven On-Demand scheme for client deployment in Federated Learning

Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process or compromising the entire model. In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture. This is a feature lacking in the initial client selection and deployment mechanism of the On-Demand architecture. We introduce a trust mechanism, named “Trusted-On-Demand-FL”, which establishes a relationship of trust between the server and the pool of eligible clients. Utilizing Docker in our deployment strategy enables us to monitor and validate participant actions effectively, ensuring strict adherence to agreed-upon protocols while strengthening defenses against unauthorized data access or tampering. Our simulations rely on continuous user behavior datasets, deploying an optimization model powered by a genetic algorithm to efficiently select clients for participation. By assigning trust values to individual clients and dynamically adjusting these values, combined with penalizing malicious clients through decreased trust scores, our proposed framework identifies and isolates harmful clients. This approach not only reduces disruptions to regular rounds but also minimizes instances of round dismissal, Consequently enhancing both system stability and security.

Opening the contextual black box: a case for idiographic experience sampling of context for clinical applications.

The experience sampling method (ESM) is increasingly used as a clinical tool in mental health care. Currently, ESM studies pay relatively little attention to assessing contextual factors, such as a person's experience and perception of events, activities, and social interactions. This has been referred to as the 'contextual black box'. However, personalized context information is essential for applications in clinical settings to gain insight in triggering and maintaining factors of psychopathology. Typically, ESM context items are designed for nomothetic research questions, to capture broad factors that are shared across individuals, such as 'unpleasant events'. We provide an overview of such nomothetic items. We argue that these items have limited clinical utility and describe idiographic alternatives to ESM context assessment to obtain more specific and personalized information about individual clients. Specifically, we present three existing idiographic ESM techniques to context assessment with clinical potential. First, we illustrate open-ended ESM items that prompt clients to fill in text, such as a description of a specific unpleasant event they experienced. Second, we describe personalized response options and self-learning items that ask clients to define personally relevant response categories, such as types of events the client finds unpleasant. Third, we describe personalized ESM items that client and clinician select or formulate together for concepts of interest. We discuss the advantages and disadvantages of these idiographic techniques. Additionally, we suggest future directions for clinical research aiming to address the 'contextual black box' and enhance the potential of ESM in mental health care.

Smartphone-assisted Language Training (SaLT) in Aphasia: Insights from the design and development of a multilingual smartphone application

Background Technology-based aphasia rehabilitation has witnessed steep growth over the past decades with the availability of several commercial apps. While such apps facilitate the intensity of rehabilitation, certain limitations including low multi-language capabilities and/or task-customization (for client-specific needs) result in lower uptake in multilingual countries like India. Aim We describe the development and pilot-testing of the Smartphone- assisted Language Training (SaLT) app – a new application for both mono- and multilingual people with aphasia that facilitates cross-lingual and cross-national sharing of intervention resources. Methods SaLT is a novel, clinician-prescribed, android-based therapy application for mono- and multilingual people with aphasia (SaLT-Home). It can be personalized and customized for individual clients through the clinician-operated web application (SaLT-SLP). Further, it permits asynchronous remote monitoring of therapy progress and sharing of therapy resources with other clinicians across the globe. We present details of the development and structure of the app. We also present pilot testing with four multilingual people with aphasia who undertook clinician-delivered therapy and SaLT-based home therapy. The clinician uploaded personalized tasks and training materials for practice into the SaLT app. Participants with aphasia and their caregivers were asked to practice tasks at home. The focus of this study was not on effectiveness but rather on usage and feasibility. We collected both quantitative and qualitative data regarding the use of the app. After the intervention, participants rated the user experience using the User Mobile Rating Scale. Outcomes & Results Three of the four participants with aphasia across two different languages were motivated to practice and used SaLT-Home. These participants were generally positive about the app. They provided useful insights regarding training in the use of app-delivered therapy, the transition to independent use of such apps and need for support during practice. Conclusions The SaLT application, with its customization and multi-language capabilities, hopes to redefine technology-based intervention in aphasia. Further, it provides the scope for collating and sharing therapy resources by clinicians around the world which could foster the rehabilitation of aphasia, globally. The data from pilot testing also provide insights into the prescription and use of mobile-based home therapy applications which is of general applicability to app-based therapy. The results of this study suggest that future exploration of the use of SaLT is warranted in experimentally controlled treatment studies.

Facilitators and Barriers in Collaborations Between Community Health Workers with Primary and Well-Being Providers in Primary Healthcare in Belgium

Background: Community health workers (CHWs) play a crucial role in bridging the gap between underserved populations and formal health. Collaborations between CHWs and health and well-being providers in primary healthcare are essential for improving access to and the quality of care for these communities. However, these partnerships require complementary strengths and specific conditions to succeed. This article addresses the limited knowledge on collaborations between CHW and primary health and well-being providers in Belgium’s CHW program. Methods: This study utilized a descriptive qualitative design. First, team collaboration data were gathered using a spreadsheet. Second, semi-structured interviews were conducted separately with 15 CHWs and 18 of their collaboration partners. The results were thematically analyzed. Results: CHWs collaborate with healthcare partners, partners with a social or societal focus, and government and educational institutions. The scope of collaborations spans sharing knowledge, connecting with the target group, and offering support to individual clients. Collaborations with healthcare providers tend to focus on individual referrals, with less reciprocity, while collaborations with other partners contribute more to outreach activities and addressing broader social determinants of health. Shared motivations and collaborative work methods facilitate collaboration, while internal organizational processes, lack of role clarity, and discrepancies can hinder successful collaboration. Conclusions: Strong local partnerships, well-defined roles, and mutual trust are essential for successful collaboration. The study findings highlight the importance of expanding collaborations to meet the diverse and intersecting needs of target groups. Effective program governance and policy are crucial in providing the flexibility necessary to address specific local requirements.

An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective

In order to address the problem of data heterogeneity, in recent years, personalized federated learning has tailored models to individual user data to enhance model performance on clients with diverse data distributions. However, the existing personalized federated learning methods do not adequately address the problem of data heterogeneity, and lack the processing of system heterogeneity. Consequently, these issues lead to diminished training efficiency and suboptimal model performance of personalized federated learning in heterogeneous environments. In response to these challenges, we propose FedPRL, a novel approach to personalized federated learning designed specifically for heterogeneous environments. Our method tackles data heterogeneity by implementing a personalized strategy centered on local data storage, enabling the accurate extraction of features tailored to the data distribution of individual clients. This personalized approach enhances the performance of federated learning models when dealing with non-IID data. To overcome system heterogeneity, we design a client selection mechanism grounded in reinforcement learning and user quality evaluation. This mechanism optimizes the selection of clients based on data quality and training time, thereby boosting the efficiency of the training process and elevating the overall performance of personalized models. Moreover, we devise a local training method that utilizes global knowledge distillation of non-target classes, which combined with traditional federated learning can effectively address the issue of catastrophic forgetting during global model updates. This approach enhances the generalization capability of the global model and further improves the performance of personalized models. Extensive experiments on both standard and real-world datasets demonstrate that FedPRL effectively resolves the challenges of data and system heterogeneity, enhancing the efficiency and model performance of personalized federated learning methods in heterogeneous environments, and outperforming state-of-the-art methods in terms of model accuracy and training efficiency.

Will the Intervention Benefit My Client? Helping Social Workers to Answer the Question Using Indicators of Clinically Meaningful Change

AbstractSocial workers strive to provide the best support to each client, making it crucial to consider not just group-level effects of interventions but also individual outcomes. While empirical evidence often stems from group-based analyses, these offer limited insights into individual change, which is key for tailoring interventions. The aim of this study is to highlight the importance of individual-level analyses, an underexplored area in social work, using four indicators of “clinically meaningful change” (CMC). We compare these analyses with group-level analyses using a dataset of 146 Swedish families undergoing Functional Family Therapy (FFT), an intervention with strong empirical support. While group-level analyses confirmed FFT’s general effectiveness in reducing adolescent psychosocial symptoms, individual-level analyses revealed a more nuanced picture, showing variations in outcomes—positive, negative, and neutral. The study thus demonstrates the value of CMC indicators for generating individual-level information that social workers can harness to find optimal solutions for their individual clients.

Emotions in Robo-Advisory: Understanding Biases, Designing Sensitive Platforms, and Enhancing User Experience through Emotional Intelligence

Introduction: Robo-advisory services revolutionize investment management by providing automated, data-centric solutions, utilizing vast data sets to tailor investment plans to individual client needs. However, the importance of emotions in investor behavior is still a crucial but sometimes disregarded feature of these systems. By using automated procedures and computational calculations to provide customized investment options, robo-advisors have completely transformed the investment management industry. Purpose: The article explores how robo-advisory services can improve investor outcomes by addressing emotional biases in investment decisions. It proposes emotionally intelligent robo- advisors that understand investor sentiment and addresses challenges, empowering investors to achieve long-term financial goals. Methodology: The article explores the challenges of integrating emotional intelligence into robo- advisory services through a comprehensive review of the existing literature, focusing on behavioral finance and empirical evidence to understand the practical implications of irrational behavior through reviewing case studies and empirical evidence. Result: The study suggests a multi-faceted approach for designing emotionally intelligent robo-advisors, considering technological innovation, regulatory compliance, ethical considerations, and user engagement strategies, addressing challenges like algorithmic biases, privacy concerns, and regulatory compliance.

Legal Problems of Compliance with Bank Secret when Performing Transactions Using Payment Cards

Payment (bank) cards are among the most actively spreading phenomena in the modern world order. However, the problems of preserving information that constitutes banking secrecy in the context of digitalization and the active spread of online payments are of particular importance. Banks are also interested in preventing the disclosure of information constituting a banking secret. However, due to the uncertainty of the current legislation, which establishes the range of information related to bank secrecy, a number of problems arise that need to be analyzed. In the presented article, the author attempts to determine whether the payment card details relate to information constituting a bank secret. Examples from judicial practice in cases arising from disputes between banks and their individual clients regarding the legality of transactions performed by the bank using payment cards are given. It is concluded that the details of the payment cards themselves do not relate to the information constituting a bank secret. They can be considered as a structural element of a banking transaction performed using a payment card.

Service Quality in Voluntary Property Insurance: A SERVQUAL-Based Approach for Poland and the USA

This study investigates the evolving dynamics of service quality in the voluntary property insurance market, focusing on the integration of Polish and USA insurance companies within their respective internal and external environments. The primary aim is to evaluate the competitiveness of this market from the perspective of individual clients, with a specific emphasis on improving service quality. Employing a SERVQUAL-based framework, this research diagnoses key factors influencing service excellence and explores the processes involved in enhancing the quality of voluntary property insurance services in both Poland and the USA.A key achievement of this work is the comprehensive analysis of literature, identifying definitional criteria and conceptual underpinnings related to service quality improvement. The findings highlight potential areas for action and unresolved issues in the ongoing efforts to elevate the standards of property insurance services. As a result, a novel model for improving the quality of insurance services has been developed and described. This model introduces a new perspective on service quality, offering new tools for measuring, analysing, and assessing the improvement process. The study provides valuable insights into service quality enhancement, offering a practical, research-driven framework that insurers and policymakers can leverage for advancing customer service standards in voluntary property insurance.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Unifying domain gap in Federated Learning: A geometric approach

Federated Learning (FL) is a framework designed to enable distributed and privacy-preserving learning. It typically involves a central server coordinating multiple clients to collaboratively train a global model without the need for sharing private data. Recent studies have revealed that this parameter-only communication strategy can be hindered by non-independent and identically distributed (non-iid) data across clients, leading to statistical heterogeneity. This issue necessitates the development of Personalized Federated Learning (PFL), which aims to enhance the performance of individual clients while still allowing for the learning of a global model. Most existing work on PFL has focused on label shift in supervised tasks, with relatively little attention given to feature shift, another common problem in real-world applications of federated learning. For instance, client data may vary in terms of illumination, angles, and quality for natural images, or in imaging protocols for medical images. To address this issue, we propose a general yet powerful framework that leverages the geometric interpretation of deep neural networks. Geometrically, a deep neural network partitions the feature space into a Power Diagram (PD) or Voronoi Diagram (VD). Regularized losses are introduced to mitigate feature shift from the perspective of PD and VD. Additionally, a personalized generator network is formulated for each client to rectify feature distribution and adapt the input data from its own domain to an ”averaged” or unified domain that generalizes well across all clients. Our method demonstrates competitive performance compared to current state-of-the-art methods.

AI-driven robo-advisors on personal financial management and investment strategies

The advent of AI-driven robo-advisors has revolutionized personal financial management and investment strategies. Leveraging sophisticated algorithms and machine learning, these platforms offer personalized financial advice and automated portfolio management with minimal human intervention. By analyzing vast datasets, including market trends, economic indicators, and individual client preferences, robo-advisors can create tailored investment plans that optimize returns and manage risks effectively. This paper explores the transformative impact of AI in democratizing financial advice, enhancing decision-making processes, and making investment management accessible to a broader audience. The integration of AI in robo-advisory services not only improves efficiency but also provides a scalable solution to meet the diverse financial needs of individuals.

Client characteristics and early working alliance development: A person-centered research approach

Objective Certain client characteristics are associated with early working alliance difficulties in psychotherapy. However, there is limited quantitative evidence on whether combinations of these characteristics (e.g., intersectional identities, prior treatment experiences) are related to alliance development. The present study leveraged a person-centered research approach to examine profiles of early alliance development and differences in the latent class structure of client characteristics among alliance development profiles. Method Individual psychotherapy clients (N = 2,579) rated the working alliance for their first four sessions and self-reported demographics, treatment history, and psychological distress. Therapists provided their assessment of clients’ primary presenting concerns at baseline. Results Latent profile analysis revealed three profiles of working alliance development: high and stable, moderate and increasing, and low and stable. Follow-up person-centered analyses (multigroup confirmatory latent class analysis) indicated that clients in the alliance profiles differed in their combinations of clinical and demographic characteristics. For example, women of color with high baseline distress and a history of prior psychotherapy were over-represented in the low and stable alliance profile. Conclusion These results are consistent with recommendations to holistically consider how clients’ characteristics and experiences shape psychotherapy processes. Results also highlight the utility of person-centered quantitative methods in psychotherapy research.

Increasing outcome measurement precision: Network analysis of items on the Outcome Questionnaire-45.

Psychotherapy outcome research mainly focuses on scale-level changes and constructs that were developed using cross-sectional statistical analysis, possibly concealing important findings on the level of single items, and limiting the clinical utility of outcome scales. Our goal was to explore changes in symptoms, interpersonal problems, and level of functioning in everyday life and to establish groups of items with similar rates of change that could be used to form more coherent targets for measuring different therapeutic outcomes. Triangulated maximally filtered graphs were used to model the network structure of the Outcome Questionnaire-45 in a data set of N = 12,075 university counseling center patients. Dynamic exploratory graph analysis was used to establish communities of items with similar rates of change. Five item communities (anxiety, hopelessness, interpersonal problems, well-being, and work impairment) were found. Compared to the original Outcome Questionnaire-45 subscales, they showed better fit to the data. The "hopelessness" community, which describes the extent of a patient's demoralization before the start of therapy, had a significantly higher rate of change compared to other communities. The discerned item communities provide clinicians with theoretically grounded, precise targets for outcome tracking, thereby enhancing the responsiveness and adaptability of treatment interventions to individual client trajectories. Such granularity enriches our understanding of therapeutic change, with direct implications for tailoring intervention strategies to maximize early therapeutic gains and sustain long-term recovery. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

FAM: Adaptive federated meta-learning for MRI data

Federated learning enables multiple clients to collaborate to train a model without sharing data. Clients with insufficient data or data diversity participate in federated learning to learn a model with superior performance. MRI data suffers from inadequate data and different data distribution due to differences in MRI scanners and client characteristics. Also, privacy concerns preclude data sharing. In this work, we propose a novel adaptive federated meta-learning (FAM) mechanism for collaboratively learning a single global model, which is personalized locally on individual clients. The learnt sparse global model captures the common features in the MRI data across clients. This model is grown on each client to learn a personalized model by capturing additional client-specific parameters from local data. Experimental results on multiple data sets show that the personalization process at each client quickly converges using a limited number of epochs. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism. Additionally, the FAM-based sparse global model has fewer parameters that require less communication overhead during federated learning. This makes the model viable for networks with limited resources.

Privacy-Enhanced Prototype-Based Federated Cross-Modal Hashing for Cross-Modal Retrieval

Cross-modal hashing is widely used for efficient similarity searches, improving data processing efficiency, and reducing storage costs. Existing cross-modal hashing methods primarily focus on centralized training scenarios, where fixed-scale and fixed-category multi-modal data is collected beforehand. However, these methods often face challenges associated with the potential risk of privacy breaches and high data communication costs during data transmission in real-world multimedia retrieval tasks. To tackle these challenges, in this article, we propose an efficient privacy-enhanced prototype-based federated cross-modal hashing (PEPFCH). In PEPFCH, we integrate local and global prototypes in order to effectively capture the distinctive traits of individual clients, while also harnessing the collective intelligence of the entire federated learning system. Moreover, to ensure the security of prototype information and prevent its disclosure during the aggregation process, we use a prototype encryption transmission mechanism to encrypt the prototype information before transmission, making it challenging for attackers to gain access to sensitive data. Additionally, to facilitate personalized federated learning and alleviate the issue of parametric catastrophic forgetting, we establish the image and text hyper-networks for each client and adopt a hyper-network extension strategy to selectively preserve and update previously acquired knowledge when acquiring new concepts or categories. Comprehensive experiments highlight the efficiency and superiority of our proposed method. To enhance research and accessibility, we have publicly released our source codes at: https://github.com/vindahi/PEPFCH .

The Impact of Therapeutic Viniyoga on State Change

While many studies have discussed the effectiveness of interventions using different yoga techniques and teachings with various populations, there has been less research on the effectiveness of yoga therapy. Viniyoga therapists utilize the principles of Viniyoga, a type of yoga developed by Krishnamacharya, to provide therapeutic yogic interventions to individual clients in order to improve functioning and relieve suffering. This qualitative study conducted interviews with fourteen Viniyoga trained yoga therapists to understand the impact of Viniyoga therapy on state change. State change is defined as either a physical or psychological change that improved well-being, ameliorated suffering, or increased insight for the client. The researchers used an in-depth semi-structured interview while utilizing open-ended questioning. A definition of Viniyoga Therapy was developed, and distinctions were made between yoga therapists and yoga teachers. Subjects interviewed discussed the results of their interventions using the 5-dimensional pañcamaya model, which considers a client’s structure, physiology, mind, personality, and emotions. Viniyoga therapists interviewed drew from their own observations, as well as clients’ self-reported data. Results were analyzed using NVivo software and based on Interpretive Phenomenological Analysis principles. Five important themes emerged: 1) Viniyoga empowers clients to participate in their own process of state change by training their self-observation skills and providing tools for influencing their state 2) Intentionally engineered experiences are the tools of Viniyoga<strong> </strong>3) Viniyoga takes a highly individualized approach to supporting state change 4) The relationship between the client and the Viniyoga therapist itself promotes state change, and is very important to the state change process 5) State change is foundational to the healing process of Viniyoga. The study concluded that Viniyoga therapy can be helpful in creating state change and alleviating distress related to a wide range of physical and psychological conditions. Implications for yoga therapy as an integrative and complementary health approach are discussed.

Uncertainty-Aware Federated Reinforcement Learning for Optimizing Accuracy and Energy in Heterogeneous Industrial IoT

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent and Identically Distributed (non-IID) datasets and straggler IoT devices, ensuring computation and communication efficiency, and addressing weight aggregation issues. In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients to effectively manage heterogeneous industrial IoT devices and improve accuracy, computation, and communication efficiency. Additionally, we introduce the Predictive Weighted Average Aggregation (PWA) method to tackle weight aggregation issues in heterogeneous industrial IoT scenarios by adjusting the weights of individual models based on their quality. The UA-FedRL addresses the inherent complexities and challenges of implementing FL in heterogeneous industrial IoT environments. Extensive simulations in complex IoT environments demonstrate the superior performance of UA-FedRL on both MNIST and CIFAR-10 datasets compared to other existing approaches in terms of accuracy, communication efficiency, and computation efficiency. The UA-FedRL algorithm attain an accuracy of 96.83% on the MNIST dataset and 62.75% on the CIFAR-10 dataset, despite the presence of 90% straggler IoT devices, attesting to its robust performance and adaptability in different datasets.

Using the FRAME Framework to Understand Therapist-Led Adaptations to the Secret Agent Society Program for Autistic Children

Autistic children often receive support for mental health and emotional challenges through community-based group-format cognitive behavioural therapy (CBT). CBT is manualized, but in community settings, interventions tend to not be delivered as intended. Inherent individual preferences and behavioural challenges in autistic children often lead to intervention adaptations that better suit individual client needs. This study used the FRAME framework to categorize and understand adaptations made to the Secret Agent Society: Small Group (SAS: SG) CBT program for autistic children (8-12 years). Interviews were conducted with therapists and then transcribed, analyzed and thematically coded using the framework. Results indicate that adaptations were primarily unplanned, content-based, and modifications were made based on clients' cognitive capacity. These results can provide useful information to help identify adaptations that do not interfere with the effectiveness of programming.

Understanding adaptive responses in PrEP service delivery in Belgian HIV clinics: a multiple case study using an implementation science framework.

In Belgium, oral HIV pre-exposure prophylaxis (PrEP) is primarily provided in specialized clinical settings. Optimal implementation of PrEP services can help to substantially reduce HIV transmission. However, insights into implementation processes, and their complex interactions with local context, are limited. This study examined factors that influence providers' adaptive responses in the implementation of PrEP services in Belgian HIV clinics. We conducted a qualitative multiple case study on PrEP care implementation in eight HIV clinics. Thirty-six semi-structured interviews were conducted between January 2021 and May 2022 with a purposive sample of PrEP care providers (e.g. physicians, nurses, psychologists), supplemented by 50 hours of observations of healthcare settings and clinical interactions. Field notes from observations and verbatim interview transcripts were thematically analysed guided by a refined iteration of extended Normalisation Process Theory. Implementing PrEP care in a centralized service delivery system required considerable adaptive capacity of providers to balance the increasing workload with an adequate response to PrEP users' individual care needs. As a result, clinic structures were re-organized to allow for more efficient PrEP care processes, compatible with other clinic-level priorities. Providers adapted clinical and policy norms on PrEP care (e.g. related to PrEP prescribing practices and which providers can deliver PrEP services), to flexibly tailor care to individual clients' situations. Interprofessional relationships were reconfigured in line with organizational and clinical adaptations; these included task-shifting from physicians to nurses, leading them to become increasingly trained and specialized in PrEP care. As nurse involvement grew, they adopted a crucial role in responding to PrEP users' non-medical needs (e.g. providing psychosocial support). Moreover, clinicians' growing collaboration with sexologists and psychologists, and interactions with PrEP users' family physician, became crucial in addressing complex psychosocial needs of PrEP clients, while also alleviating the burden of care on busy HIV clinics. Our study in Belgian HIV clinics reveals that the implementation of PrEP care presents a complex-multifaceted-undertaking that requires substantial adaptive work to ensure seamless integration within existing health services. To optimize integration in different settings, policies and guidelines governing PrEP care implementation should allow for sufficient flexibility and tailoring according to respective local health systems.