Service Requests Research Articles

Reduction in Aedes aegypti Population After a Year-Long Application of Targeted Sterile Insect Releases in the West Valley Region of Southern California

Aedes aegypti is of great public health concern because of its vectorial capacity to transmit various arboviruses such as Zika, yellow fever, dengue, and chikungunya. In California, its expanding geographic distribution has been unrestrained. This urgently calls for innovative tools such as the use of sterile insect technique (SIT) to strengthen invasive Aedes control. This study aimed to determine the efficacy of combining an SIT application into integrated vector management (IVM) strategies in invasive Aedes control in the West Valley region of southern California. A total of 25 Aedes hotspots, grouped into two cohorts—sites that received SIT treatment only (n = 9) and sites that received both SIT and In2Care® Mosquito Stations (n = 16)—were selected for this study. Biweekly, X-ray irradiated male Ae. aegypti mosquitoes were released between April and November 2024. Data from weekly BG Sentinel-2 traps were utilized to compare the mosquito densities between the pre-intervention (2023) and intervention (2024) periods for both cohorts. In addition, a subset of samples (n = 50) of irradiated male mosquitoes were placed in a separate cage with freshly emerged unirradiated female mosquitoes to evaluate the level of sterility of the eggs from these mosquitoes. Aedes-related service requests made by the District’s residents were also compared between the pre-intervention and intervention periods to indirectly determine the effect of optimizing the control strategies on overall mosquito problems. A total of 106,608 sterile male Ae. aegypti were released between April and November 2024. The cohort with the SIT only application showed a 44% reduction in the number of female Ae. aegypti per trap-night during the intervention period compared with the pre-intervention period. At sites with In2Care Mosquito Stations that also received SIT treatment, the mosquito density dropped by 65% during the intervention period compared with the pre-intervention period. The number of Aedes-related service requests during the intervention year (n = 367) was 45% lower than the pre-intervention year (n = 656). Over 99.6% eggs collected from female mosquitoes mated with irradiated males did not hatch, indicating a high level of sterility of the irradiated males utilized for our SIT application. Here, we demonstrated the potential of SIT when combined with IVM strategies such as In2Care® Mosquito Stations, offering a holistic approach to reducing the public health risks associated with Aedes-borne diseases.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages.

A learning and searching integration approach for same-day delivery problem with heterogeneous fleets considering uncertainty

Rising customer expectations for fast shipping, has significantly fuelled the widespread popularity of same-day delivery services. To optimise these services for dynamic and stochastic requests, employing heterogeneous fleets that include both vehicles and drones can effectively minimise the resources required for delivery. However, uncertainties in the real world, particularly in vehicle travel, adversely affect the service levels. This paper introduces a same-day delivery problem with heterogeneous fleets considering uncertain travel and service time (SDDPHF-UT). We utilise a route-based Markov decision process to formulate this problem, explicitly incorporating travel and service uncertainty into the state and action spaces. Next, we develop an adaptive optimisation approach integrating learning and searching (AILS) to solve the problem. This approach employs policy proximal optimisation and dynamic variable neighbourhood search, responsible for request assignments and vehicle route planning, respectively. These components are interdependent and collaborate to tackle the problem in a synchronised manner, enhancing adaptability to dynamic and stochastic scenarios. Computational results indicate that the AILS significantly outperforms baseline approaches, exhibiting robust generalisation across various data distributions and fleet sizes, and achieving an average service rate improvement of 7.23%. This underscores AILS' potential as a highly effective tool of optimising same-day delivery operations in the face of uncertainty.

Factors Associated with Tobacco Cessation Services Request Among Users of an Online Self-Screening Questionnaire

Objectives Tobacco smoking remains a major public health risk, responsible for millions of deaths worldwide. While smoking patterns in Mexico differ from those in countries with higher rates, comorbidities such as diabetes pose a health risk. Although many smokers want to quit, access to cessation services is limited. Internet-based cessation (I-BC) services are a promising modality that offers accessibility and machine learning (ML) has been successfully used to predict tobacco outcomes. This study uses ML to identify characteristics associated with requesting I-BC through an online self-assessment questionnaire in Mexico. Methods This was a retrospective, predictive, secondary analysis of 14,182 records of individuals aged 18 years and older who completed an online screening for nicotine dependence and their request for tobacco cessation services. Random forest algorithm with four oversampling methods was compared to select the best predictive model. The relative importance of predictor variables was measured as well. Results The algorithm had a sensitivity of 78.6% and a specificity of 68.8%. Specifically, age, sex, dependence severity indicators, locations such as the state of Mexico or Sinaloa, and even occasions such as World No Tobacco Day were identified as key factors influencing cessation service requests. Conclusions These results suggest the random forest algorithm’s effectiveness in predicting potential cessation service users. Furthermore, the predictor variables provide valuable insights for designing targeted prevention and awareness campaigns, potentially leading to improved campaign effectiveness and more individuals receiving cessation support.

Digital Marketing Activities and Innovative Forms of Using Archival Information

The aim of the study is to identify peculiarities and content of digital marketing methods of information activities and innovative forms of using archival information. Research methods. The study is a complex of various methods of cognition and examination of the object-subject component of the problem. The methods of scientific analysis and synthesis contribute to comprehensive review of sources and literature on the issue, the discovery of information on the sought problem and the text design. The overview and analytical method is used to study the main concepts of the work and survey the current discourse of information activity. The historical method makes it possible to consider the object-subject component in retrospect, and to identify the main forms of archival information usage. The method of analytical Internet monitoring is used to examine the information of e-resources, to identify peculiarities of storage, transmission and presentation of archival e-documents. The scientific novelty consists in the necessity of a comprehensive analysis and scientific-theoretical grounding of digital marketing activity, and the introduction of the latest forms of archival information using. Identifying the innovative marketing tendencies of information activities and the active introducing of information technologies into the archival sphere determine the future development of the industry, as well as contribute to the improvement of e-archival communication and its integration into the global information space. Main conclusions. The improvement of information technologies and the spread of means of information and communication activities in the archival sphere determine the latest tendencies in the field of archival information use. In conditions of e-archive development intensification and filling of archival web resources, a positive dynamics in the number of users and requests for services is observed. One of the main trends and prospects for the development of informational social communications is promotion in social networks, the effective use of e-marketing, the involvement of interactive methods, the spread of influence marketing and the applying of influencers, as well as informative and relevant content marketing taking into account current informational needs of society.

On the Concept of Opportunity Cost in Integrated Demand Management and Vehicle Routing

Integrated demand management and vehicle routing problems are characterized by a stream of customers arriving dynamically over a booking horizon and requesting logistical services, fulfilled by a given fleet of vehicles during a service horizon. Prominent examples are attended home delivery and same-day delivery problems, where customers commonly have heterogeneous preferences regarding service fulfillment and requests differ in profitability. Thus, demand management methods are applied to steer the booking process to maximize total profit considering the cost of the routing decisions for the resulting orders. To measure the requests’ profitability for any demand management method, it is common to estimate their opportunity cost. In the context of integrated demand management and vehicle routing problems, this estimation differs substantially from the estimation in the well-examined demand management problems of traditional revenue management applications as, for example, found in the airline or car rental industry. This is because of the unique interrelation of demand control decisions and vehicle routing decisions as it inhibits a clear quantification and attribution of cost, and of displaced revenue, to certain customer requests. In this paper, we extend the theoretical foundation of opportunity cost in integrated demand management and vehicle routing problems. By defining and analyzing a generic Markov decision process model, we formally derive a definition of opportunity cost and prove opportunity cost properties on a general level. Hence, our findings are valid for a wide range of specific problems. Further, based on these theoretical findings, we propose approximation approaches that have not yet been applied in the existing literature, and evaluate their potential in a computational study. Thereby, we provide evidence that the theoretical results can be practically exploited in the development of solution algorithms. Funding: This work was supported by the University of the Bundeswehr Munich. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0644 .

Unlocking User Satisfaction: A Delone & Mclean Is Success Model Approach To It Helpdesk Ticketing System Adoption

The IT Helpdesk Ticketing System application is used to submit requests for IT services and handle technical problems related to these services. This research aims to understand the factors that influence user satisfaction with this application, which is triggered by user dissatisfaction. The research method used is qualitative using the DeLone & McLean IS Success Model, which is implemented through the Partial Least Squares Structural Equation Modeling (SEM) framework. The research was conducted on 231 respondents who were surveyed online. The results showed that system quality, service quality, and level of system usage significantly affect user satisfaction. System and service quality contribute positively to user satisfaction, while the level of system usage also has a key role in determining the level of satisfaction. The implications of the findings emphasize the importance of paying attention to these aspects in system development and improvement, while increasing the level of usage to meet the needs and increase user satisfaction more effectively.

SimPy Simulation to Optimize Remote Patient Monitoring and Server Usage in Healthcare System

The recent pandemic has shown us the necessity of handling a huge mass of patients in real-time scenarios. This is important because in real-time scenarios when the entire healthcare system is operating in its full swing to deliver services to the patient, it is equally important to monitor the arrival time of the patient at the hospital till the disposal of a patient from the hospital. Hence, that each patient must wait minimum time frame there by using the existing remote healthcare infrastructure. To perform this micro level (i.e. patient level) monitoring of the remote healthcare ecosystem, an integrated cloud-based healthcare system (CHS) should be proposed which will analyse, simulate, and predict a huge load of service requests of a patient. At the same time, it can prepare the entire healthcare ecosystem to face the real-time need of any nation and deliver corresponding service response to every patient in a fastest possible manner. In this regard, Discrete event simulation in SimPy (DES) methodology is of utmost help to the amplification of remote healthcare infrastructure on a real scale. In this report, authors have found 80 percent server efficiency and 1.4 s waiting time average for each patient in the proposed model. Server efficiency and patient waiting time mapping typically has become very promising action for any remote cloud-assisted healthcare service delivery model. Idle time analysis of servers can stimulate remote resource utilization and scalability in patient modelling.

Appropriately Matching Transport Care Units to Patients in Interhospital Transport Care: Implementation Study.

In interfacility transport care, a critical challenge exists in accurately matching ambulance response levels to patients' needs, often hindered by limited access to essential patient data at the time of transport requests. Existing systems cannot integrate patient data from sending hospitals' electronic health records (EHRs) into the transfer request process, primarily due to privacy concerns, interoperability challenges, and the sensitive nature of EHR data. We introduce a distributed digital health platform, Interfacility Transport Care (ITC)-InfoChain, designed to solve this problem without compromising EHR security or data privacy. This study aimed to detail the implementation of ITC-InfoChain, a secure, blockchain-based platform designed to enhance real-time data sharing without compromising data privacy or EHR security. The ITC-InfoChain platform prototype was implemented on Amazon Web Services cloud infrastructure, using Hyperledger Fabric as a permissioned blockchain. Key elements included participant registration, identity management, and patient data collection isolated from the sending hospital's EHR system. The client program submits encrypted patient data to a distributed ledger, accessible to the receiving facility's critical care unit at the time of transport request and emergency medical services (EMS) teams during transport through the PatienTrack web app. Performance was evaluated through key performance indicators such as data transaction times and scalability across transaction loads. The ITC-InfoChain demonstrated strong performance and scalability. Data transaction times averaged 3.1 seconds for smaller volumes (1-20 transactions) and 6.4 seconds for 100 transactions. Optimized configurations improved processing times to 1.8-1.9 seconds for 400 transactions. These results confirm the platform's capacity to handle high transaction volumes, supporting timely, real-time data access for decision-making during transport requests and patient transfers. The ITC-InfoChain platform addresses the challenge of matching appropriate transport units to patient needs by ensuring data privacy, integrity, and real-time data sharing, enhancing the coordination of patient care. The platform's success suggests potential for regional pilots and broader adoption in secure health care systems. Stakeholder resistance due to blockchain unfamiliarity and data privacy concerns remains. Funding has been sought to support a pilot program to address these challenges through targeted education and engagement.

Comprehensive Towing Services Online

Abstract: This project aims to streamline and enhance the towing service experience through the development of a comprehensive web-based platform. Utilizing JavaScript, HTML, CSS, XAMPP, phpMyAdmin, and SQL, the platform offers a user-friendly interface that facilitates efficient towing services. JavaScript ensures dynamic client-side interactions, while HTML and CSS provide a clean and intuitive layout. XAMPP serves as the local server environment, and phpMyAdmin manages SQL database interactions. The backend, powered by SQL, securely handles user data, service requests, and provider details. Users can easily request towing services, track their status, and access service history, while providers can manage their fleet and respond to requests efficiently. This project represents a significant advancement in the towing service industry, offering a seamless solution that benefits both users and service providers through its robust technological foundation.

An In-Depth Analysis of the Mobile Application Rescuennect Leveraging Telematics Technology for Roadside Assistance and Emergency Response Solutions

This paper presents the development of Rescuennect, a mobile application designed to enhance roadside assistance and emergency response services on Siargao Island using telematics technology. The application integrates GPS and Google Maps API to provide real time tracking, service availability, and a directory of roadside assistance and emergency response services. Users can request services such as battery jumps, flat tire changes, gasoline delivery, and towing with a few taps while tracking response times and the progress of assistance personnel in real time. Additionally, Rescuennect updates traffic conditions, road closures, and nearby points of interest. Developed using the Rapid Application Development (RAD) model and evaluated using ISO 9126 standards, Rescuennect achieved high scores in usability (4.37), functionality (4.13), and maintainability (4.20). This integrated approach promises to revolutionize Siargao Island's roadside assistance and emergency response landscape, providing faster, more reliable, and transparent services for residents and tourists.

Optimizing same-day delivery with vehicles and drones: A hierarchical deep reinforcement learning approach

The advent of same-day delivery services has achieved immense popularity, driven by escalating customer expectations on fast shipping and the need for market competitiveness. To optimize such services, the use of heterogeneous fleets with vehicles and drones has proven effective in reducing the resource requirements needed for delivery. This paper focuses on investigating the same-day delivery dispatching and routing problem with a fleet of multiple vehicles and drones. In this problem, stochastic and dynamic requests, coupled with their stringent time constraints, require dispatchers to make real-time decisions about optimally assigning vehicles and drones, ensuring both efficiency and effectiveness in delivery operations while taking into account the routing. To tackle this complex problem, we model it with a route-based Markov decision process and develop a novel hierarchical decision approach based on deep reinforcement learning (HDDRL). The first level of the hierarchy is tasked with determining the departure times of vehicles, balancing the trade-offs between the delivery frequency and efficiency. The second level of the hierarchy is dedicated to determining the most suitable delivery mode for each request, whether by vehicles or drones. The third level is responsible for planning routes for vehicles and drones, thereby enhancing route efficiency. These three levels in the hierarchical framework collaborate to solve the problem in a synchronized manner, with the objective of maximizing the service requests within a day. Empirical results from computational experiments highlight the superiority of the HDDRL over benchmark methods, demonstrating not only its enhanced efficacy but also its robust generalization across diverse data distributions and fleet sizes. This underscores the HDDRL’s potential as a powerful tool for enhancing operational efficiency in same-day delivery services.

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio.

Enhancing operational efficiency: implementing business process improvement by integrating the command center in the Indonesian toll road operator company

<p>Business process improvement is a well-known technique for organizations to identify processes that enhance business performance. This study aims to analyze and improve business processes in the company's principal operational processes that are directly related to controlling service requests for assistance with traffic problems on the toll road (Command Center), specifically in communication and handling requests for assistance and complaints from users of one of the toll road operators in Indonesia. Research is conducted by identifying existing processes, the underlying causes of existing problems, and analyzing business processes using the Business Process Improvement methodology and the Bizagi Modeler with Process Modeling and Notation (BPMN). An improvement within this approach is thus examined in a particular scenario. The as-is and to-be processes are measured using a technique of improvement design called the ESIA approach, consisting of four steps: eliminate, simplify, integrate, and automate. The result of the integration of Command Center services can decrease the response time for assisting requests from toll road users and enhance cost-effectiveness, improving customer satisfaction and the overall quality of toll road service.<em></em></p>

Crime Scene: A Mystic Place

This document highlights the essential role of Crime Scene Investigation (CSI) in modern forensic science, emphasizing the need for systematic protocols to preserve evidence integrity. In the U.S., a request for CSI services acts as a formal agreement, requiring a thorough evaluation of the requesting agency’s needs and the lab’s capabilities, a process that differs significantly in India. Effective CSI involves identifying the scene, collecting and securing evidence, and reconstructing events to establish facts. Advanced forensic techniques, including DNA analysis and comprehensive documentation, enhance the credibility of evidence, transforming speculation into definitive conclusions about guilt or innocence. The document addresses challenges such as human interference, which necessitates rigorous protocols to protect evidence and maintain scene integrity. Meticulous documentation is vital, as even small oversights can undermine investigations. It also examines the impact of technological advancements, particularly the rise of cybercrime, which introduces unique challenges due to anonymity. Additionally, it underscores the legal obligations of investigators, focusing on the critical need to maintain a clear chain of custody. Recent court rulings emphasize evolving evidence collection standards, especially for digital data, which must be handled carefully to safeguard privacy. Ultimately, robust crime scene management and scientific analysis are essential for delivering justice in both conventional and cybercrime cases.

Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines

As the number of intelligent devices in mining environments increases, the transmission time for large datasets, including equipment status and environmental parameters, also rises. This increase leads to longer response times for service requests, making it difficult to meet the equipment’s real-time requirements. Edge computing effectively addresses the demands for low latency and high performance. However, the deployment of edge nodes can negatively affect overall service performance due to resource limitations and node heterogeneity. In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. Additionally, we developed a simulation platform based on a production line system and an intelligent patrol vehicle to evaluate the proposed method’s effectiveness. The experimental results show that the IBGA and ISCSO algorithms effectively reduce task delay and deployment cost. Both deployment methods outperform the benchmark algorithms and offer better service quality assurance.

Collaboration Across Disciplines to Integrate Clinical Expertise into Medical Software Development: The Approach of the Dedalus Medical Office.

Medical informatics is a multidisciplinary field combining clinical and technical expertise. Addressing the challenge of aligning software design with clinicians' real-world needs, Dedalus established the Medical Office, a dedicated department designed to integrate clinical expertise directly into the software development process, in 2022. This paper details the approach and impact of the Medical Office. An international team of 15 healthcare professionals with experience in medical informatics was assembled. The team employed a multifaceted approach, incorporating global communication sessions and a ticketing system to track and analyze service requests. Over two years, 398 tickets were received, categorized into nine areas: clinical content curation, medical terminologies, clinical safety, clinical evaluation, design support, clinical UX, research & publication, real-world medical cases, and pre-sales support. The average duration of ticket resolution decreased over time, attributed to process fine-tuning and the formation of a relevant expert group. A preliminary satisfaction survey indicated positive feedback from technical teams. The collaborative model improved software design, usability, and clinical safety, demonstrating the value of clinician involvement. While preliminary results are promising, ongoing evaluation and adaptation are essential. The study emphasizes the importance of interdisciplinary collaboration in medical informatics and the benefits of clinician involvement in healthcare technology development. Future studies should explore this model's long-term impacts and scalability in other organizations and healthcare systems.

A Network Calculus Model for SFC Realization and Traffic Bounds Estimation in Data Centers

Network Function Virtualization (NFV) is a promising technology that can transform how internet service providers deliver their services. However, recent studies have identified several challenges in adopting NFV. Two key challenges are central to the operation and capacity planning of NFV Data Centers (DCs): (i) Service Function Chain (SFC) realization —determining if a new request with a known profile can be accommodated—and (ii) Network Function Virtualization (NFV) traffic bounds estimation —estimating the total traffic that a data center can handle considering all service requests and their performance constraints. To address these challenges, we propose a model that leverages stochastic network calculus to effectively dimension an NFV DC while ensuring delay and availability bounds for all service requests. Our theoretical model provides a mathematical framework to assess the realization of a single SFC request without delving into the specifics of the realization process. We utilize established availability-aware Virtual Network Function (VNF) placement patterns to obtain traffic bounds essential to planning data center capacity. We analyze NFV data center traffic under various scenarios over a Fat-tree DC topology. The results demonstrate that data center capacity is significantly influenced by the VNF placement strategy. Additionally, for data centers hosting latency-sensitive services, Service Level Objective (SLO) constraints on availability and delay are crucial in determining the number of such requests that can be accommodated.

Examining mental health engagement among veterans diagnosed with serious mental illness.

Engagement in psychosocial mental health services has been found to support and facilitate the process of recovery in individuals with serious mental illness. However, many individuals eligible for these services are not receiving them. Presently, little is known about the factors that contribute to treatment engagement. This study aimed to identify attitudes related to treatment engagement among veterans with serious mental illness enrolled at a large VA medical center. Thirty-six veterans participated in individual qualitative interviews about their beliefs about the efficacy and purpose of mental health care; their perceptions of societal attitudes regarding mental health; and their experiences accessing mental health care. Based on their level of treatment engagement in the last year, veterans were classified as either engaged in psychosocial mental health services (n = 15), engaged in psychiatric medication management only (n = 14), or not engaged in VA mental health services (n = 7). Attitudes associated with level of service usage were examined to identify factors that might improve engagement in treatment and quality of mental health services. Results suggest that provider expertise and rapport were critical to engagement. Our findings also suggest that barriers to accessing care included beliefs about when it was appropriate to request services, stigmatizing attitudes within one's social network, the complexity of the mental health system, and failures of providers and the treatment system to address the needs of a diverse patient population. Implications for clinical training and practice are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).