Optional Components Research Articles

A Guide to the Implementation and Design of Ex Vivo Perfusion Machines for Vascularized Composite Allotransplantation.

Ex vivo machine perfusion (EVMP) is a versatile platform utilized in vascularized composite allotransplantation (VCA) to prolong preservation, salvage tissue, and evaluate graft viability. However, there is no consensus on best practices for VCA. This article discusses the common components, modifications, and considerations necessary for a successful VCA perfusion. A systematic literature review was performed in several databases (PubMed, Scopus, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov) to identify articles published on VCA EVMP (face, limb, abdominal wall, uterus, penis, and free flaps) before August 2022. Graft type and animal model, general perfusion parameters, core components of the circuit, and optional components for enhanced monitoring were extracted from the articles. A total of 1370 articles were screened, and 46 articles met inclusion criteria. Most articles (84.8%) were published in the last 10 years. Pigs were the main model used, but 10 protocols used human grafts. Free flaps were the most common graft type (41.3%), then upper extremities/forelimbs (28.3%), uteruses (17.4%), and hindlimbs (13.0%). Postperfusion replantation occurred in 15.2% of studies. Normothermic perfusion predominated (54.1%), followed by hypothermic (24.3%), and subnormothermic (21.6%). The majority of studies (87.0%) oxygenated their systems, most commonly with carbogen. EVMP is a rapidly growing area of research. Leveraging EVMP in VCA can optimize VCA procedures and allow for expansion into replantation, flap salvage, and other areas of plastic surgery. Currently, VCA EVMP is achieved through a variety of approaches, but standardization is necessary to advance this technology and attain clinical translation.

The Impact of Managerial Accounting Techniques on Short-term Decisions

This study aims to determine the importance of managerial accounting in the process of short-term decision-making. Since managerial accounting is a crucial component of the financial information system and is crucial in making decisions in big companies or SMEs, while managerial accounting is only briefly discussed in the majority of economic units, there are some organizations where certified public accountants view managerial accounting as an optional component of the business. Financial data was collected from one of the SME companies in KRG/Sulaimani governorate, which is Mariwan Gallery, located in Bazian. The results indicate that managerial accounting has a significant effect on the process of short-term decision-making. This study recommends that all businesses, including small shops, hire accountants in order to make critical financial decisions. This helps businesses perform better, and it also provides more job opportunities for society.

Process evaluation of a randomised controlled trial - prevention of sickness absence through early identification and rehabilitation of at-risk patients with musculoskeletal disorders (PREVSAM)

BackgroundMusculoskeletal disorders are commonly treated in primary healthcare and may, if not treated adequately, entail a risk for long-term disability and sickness absence. A team-based rehabilitation intervention (PREVention of Sickness Absence for Musculoskeletal disorders, PREVSAM) was evaluated in a randomised controlled trial. The purpose of this study was to evaluate the process of implementing the PREVSAM model in primary care rehabilitation.MethodsThis process evaluation was conducted alongside the trial, collecting quantitative and qualitative data to evaluate how the PREVSAM model was implemented, mechanisms of impact, and contextual factors. Acceptability, feasibility, appropriateness, adaptations, training and support, resources, recruitment, reach, retention, dose, fidelity, and readiness for change were investigated. Qualitative data were collected from healthcare professionals and patients.ResultsEight of 22 invited rehabilitation clinics (36%) and 28 of 54 healthcare professionals (52%) were included in the PREVSAM trial and this process evaluation. Of 507 eligible patients, 261 (51%) were included. Of those, 134 were randomised to the intervention and 129 (96%) were retained. Twelve healthcare professionals and 15 patients participated in the qualitative evaluations. The model’s essential components; individual assessments and structured, team-based rehabilitation with clear division of responsibilities agreed in a joint health plan; were generally delivered according to protocol. The optional components early access to psychological treatment and workplace contact were delivered to a lesser extent. Perceived acceptability, feasibility, and appropriateness of the PREVSAM model were moderate to high. Several contextual barriers, in the form of missing prerequisites, affected the implementation. Qualitative data showed that the model, with its holistic view, was appreciated by both healthcare professionals and patients.ConclusionsThis process evaluation suggests that PREVSAM is acceptable, feasible and appropriate for patients with MSDs reporting psychological risk factors associated with increased risk for sickness absence. While essential components were implemented with fidelity for most patients, optional components were not. This variability reflects the complexity of the model, its mandatory and optional components, contextual barriers, and the person-centred approach meeting individual patient needs. As all model components were not delivered to all patients, the intervention may have been too similar to treatment as usual to detect differences on a group level. A limitation of the study is that half of the participating rehabilitation clinics withdrew prematurely.

Rapid Development of A Spatial Digital Twins Using Open-source Solutions

Abstract. City-model spatial Digital Twins are a three-dimensional virtual self-updating twin of a city which is updated by real-time data. This paper explores the effectiveness of open-source data and tools that can generate a Digital Twin to be hosted on a distributed system. A spatial Digital Twin was successfully generated from a city-model made from open-source spatial data with access to real-time data. The Digital Twin application was hosted on a three-tier systems architecture. The exception to the open-source data was a Digital Surface Model (DSM) obtained from a private source. However, the DSM was an optional component. The output only met the minimum requirements of a Digital Twin while the visualization was basic where the live data was limited to global weather data rather than city-specific data. The investigation indicated that even with little data, generating a 3D city scene with an automated live data flow meets the spatial Digital Twin’s minimum requirements according to Piroumian (2023). The main contribution of the study is highlighting methods for generating and hosting a spatial Digital Twin and its associated data onto the web through using a three-tier architecture to store, host and display the Twin onto the web. The areas identified for further research include investigating variations in the distributed systems for hosting and publishing a spatial Digital Twin onto the web. A secondary subject of investigation is exploring methodologies for improving the detail of the spatial Digital Twin’s data for areas that do not receive much live data.

Implementation of a bundle to improve diagnosis in hospitalized patients: lessons learned.

The inpatient setting is a challenging clinical environment where systems and situational factors predispose clinicians to making diagnostic errors. Environmental complexities limit trialing of interventions to improve diagnostic error in active inpatient clinical settings. Informed by prior work, we piloted a multi-component intervention designed to reduce diagnostic error to understand its feasibility and uptake. From September 2018 to June 2019, we conducted a prospective, pre-test/post-test pilot study of hospital medicine physicians during admitting shifts at a tertiary-care, academic medical center. Optional intervention components included use of dedicated workspaces, privacy barriers, noise cancelling headphones, application-based breathing exercises, a differential diagnosis expander application, anda checklist to enable a diagnostic pause. Participants rated their confidence in patient diagnoses and completed a survey on intervention component use. Data on provider resource utilization and patient diagnoses were collected, and qualitative interviews were held with a subset of participants in order to better understand experience with the intervention. Data from 37 physicians and 160 patients were included. No intervention component was utilized by more than 50 % of providers, and no differences were noted in diagnostic confidence or number of diagnoses documented pre-vs. post-intervention. Lab utilization increased, but there were no other differences in resource utilization during the intervention. Qualitative feedback highlighted workflow integration challenges, among others, for poor intervention uptake. Our pilot study demonstrated poor feasibility and uptake of an intervention designed to reduce diagnostic error. This study highlights the unique challenges of implementing solutions within busy clinical environments.

MENTAL HEALTH-ILLNESS PROCESS IN THE CONCEPTION OF PSYCHOLOGY GRADUATES

The objective was to understand, based on the narratives of students from an undergraduate psychology course, the social representation regarding the mental health and illness process. This is narrative research carried out at a public university in Bahia. Data collection took place online, using an electronic form made available in 2021, for data analysis dialectical hermeneutics was used. 34 students participated in the study. The results were presented in two empirical categories: biomedical model in the mental health and illness process and social determinants model in the mental health and illness process. The students narrated their social representation about the mental health and illness process, both from a perspective that favors the biomedical model and based on the psychosocial care model. Considering the analysis carried out, there is an urgent need for undergraduate courses in Psychology and other courses in the health field to review their curricula in order to meet the precepts of the Brazilian Psychiatric Reform, making use of mandatory and optional components with active methodologies of teaching-learning that address the topic, thus aiming to resolve stigmatized conceptions in the field of mental health and prepare professionals who base their work on the precepts of psychosocial care.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

Advancing breast ultrasound diagnostics through hybrid deep learning models

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the “EfficientKNN” model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms—SGD, Adam, and RMSprop—with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.

Telemedicine: Transforming patient-provider relationships in the digital age

Telemedicine has emerged as a crucial component of modern healthcare, offering patients and providers new options for interaction and care delivery. This comprehensive narrative review explores the evolution of telemedicine in the digital age, highlighting its importance in transforming healthcare. Advancements in technology, such as artificial intelligence, machine learning, and the Internet of Things, have revolutionized telemedicine, enhancing accessibility, efficiency, and accuracy in patient care. We examine key areas such as the enhancement of healthcare access for rural and underserved populations, the convenience of reduced travel time and waiting periods, and the cost-effectiveness for both patients and providers. The review also covers telemedicine applications in various medical specialties, including primary care, mental health, and chronic disease. Despite its rapid growth and widespread adoption, significant gaps in the literature persist, particularly concerning the long-term impacts on patient-provider relationships, clinical outcomes, and the overall quality of care. Our paper aims to address these gaps by providing a detailed review of the current state of telemedicine, its benefits, and its challenges. Additionally, we discuss the technological barriers, regulatory and legal challenges, and potential drawbacks in patient-provider communication that telemedicine encounters. Future directions and innovations are explored, focusing on emerging technologies, integrating electronic health records, and personalized telemedicine services tailored to individual patient needs. The paper concludes with clinical perspectives, emphasizing the need for continuous research to optimize its implementation. By addressing these critical aspects, our review provides a comprehensive understanding of telemedicine's role in the digital age and its promising future in the healthcare landscape.

Patients as teachers: a within-subjects randomized pilot experiment of patient-led online learning modules for health professionals

PurposeMany health professions education programs involve people with lived experience as expert speakers. Such presentations may help learners better understand the realities of living with chronic illness or experiencing an acute health problem. However, lectures from only one or a small number of people may not adequately illustrate the perspectives and experiences of a diverse patient cohort. Additionally, logistical constraints such as public health restrictions or travel barriers may impede in-person presentations, particularly among people who have more restrictions on their time. Health professions education programs may benefit from understanding the potential effects of online patient-led presentations with a diverse set of speakers. We aimed to explore whether patient-led online learning modules about diabetes care would influence learners’ responses to clinical scenarios and to collect learners’ feedback about the modules.MethodThis within-subjects randomized experiment involved 26 third-year medical students at Université Laval in Quebec, Canada. Participation in the experiment was an optional component within a required course. Prior to the intervention, participating learners responded to three clinical scenarios randomly selected from a set of six such scenarios. Each participant responded to the other three scenarios after the intervention. The intervention consisted of patient-led online learning modules incorporating segments of narratives from 21 patient partners (11 racialized or Indigenous) describing why and how clinicians could provide patient-centered care. Working with clinical teachers and psychometric experts, we developed a scoring grid based on the biopsychosocial model and set 0.6 as a passing score. Independent evaluators, blinded to whether each response was collected before or after the intervention, then scored learners’ responses to scenarios using the grid. We used Fisher’s Exact test to compare proportions of passing scores before and after the intervention.ResultsLearners’ overall percentage of passing scores prior to the intervention was 66%. Following the intervention, the percentage of passing scores was 76% (p = 0.002). Overall, learners expressed appreciation and other positive feedback regarding the patient-led online learning modules.DiscussionFindings from this experiment suggest that learners can learn to provide better patient-centered care by watching patient-led online learning modules created in collaboration with a diversity of patient partners.

Psychedelic-assisted Therapy Training: Firsthand Experience of Non-Ordinary States of Consciousness in the Development of Competence

Introduction This review explores the benefits of incorporating personal experience(s) with non-ordinary states of consciousness as a core component of Psychedelic-Assisted Therapy (PaT) training.The program incorporates an optional experiential training component. We collaborate with professionals affiliated with a Canadian non-profit organization specializing in PaT experiential training. As do other stakeholders in this field– including program developers, educators, and researchers–we navigate a rapidly evolving and often ambiguous landscape, where infrastructure and regulations are lagging scientific data and best practices. Given the potential for differing perspectives, the authors acknowledge that their personal experiences could be a potential source of bias, influencing objectivity.ObjectivesConversely, these lived experiences could be seen as valuable contributions, enriching perspectives on the role of experiential training. In that context, our intention is to provide a comprehensive review, presenting arguments both in favour of and against the integration of experiential training in PaT.MethodsThere is an urgent need for establishing legal training and practice options, bridging the underground with best practices, with all practitioners operating within a regulated and ethically accountable framework. Such a proactive strategy would mitigate the risks associated with unregulated training in a field with relatively few guidelines on how to develop competency.ResultsAn in-training PaT experience supports personal comfort, self-assuredness, and confidence supporting others in non-ordinary states of consciousness, with contemporary researchers/experts highlighting the specific challenges among therapists who lack lived experience. These might include holding unrealistic expectations, being unaware of the impacts of set and setting, and misunderstandingConclusions Whether or not therapists engage in experiential training - serving a dual in one’s own healing process, it is imperative that they maintain their own wellness practices. This proactive/primary prevention strategy would improve well-being and resilience, reducing secondary mental health consequences for patients and providers. Cultivating a culture of self-care within the mental health field should be an overarching priority for training programs and professional organizations, without which we are left with broken people in support of broken people. Current rates of burnout, absenteeism and early retirement suggest that we are already on that trajectory and should serve as a call to action.Disclosure of InterestNone Declared

The impact and relevance of techniques and fluids on lung injury in machine perfusion of lungs.

Primary graft dysfunction (PGD) is a common complication after lung transplantation. A plethora of contributing factors are known and assessment of donor lung function prior to organ retrieval is mandatory for determination of lung quality. Specialized centers increasingly perform ex vivo lung perfusion (EVLP) to further assess lung functionality and improve and extend lung preservation with the aim to increase lung utilization. EVLP can be performed following different protocols. The impact of the individual EVLP parameters on PGD development, organ function and postoperative outcome remains to be fully investigated. The variables relate to the engineering and function of the respective perfusion devices, such as the type of pump used, functional, like ventilation modes or physiological (e.g. perfusion solutions). This review reflects on the individual technical and fluid components relevant to EVLP and their respective impact on inflammatory response and outcome. We discuss key components of EVLP protocols and options for further improvement of EVLP in regard to PGD. This review offers an overview of available options for centers establishing an EVLP program and for researchers looking for ways to adapt existing protocols.

Development, Validation, and Cross Cultural Adoption of Persian Version of Behavioral Risk Factor Tool.

One of the most crucial objectives of policymakers is to enhance the population's overall health. Establishing a surveillance system is a way to achieve this goal. The Behavioral Risk Factor Surveillance System (BRFSS) is a national system that collects data on the health-related behaviors of the United States residents using the Behavioral Risk Factor Questionnaire (BRFSSQ). This survey is aimed at reducing risk behaviors and their consequences. Regarding the fact that the cultural environment within each country may affect how behaviors are assessed, this study aimed to develop a Persian version, cross-cultural adaptation, and assess the validity and reliability of the PBRFSSQ. In this cross-sectional study, 250 individuals were enrolled using the stratified sampling method between August 2022 and April 2023. Six steps of translation and test method proposed by Sousa et al was used. Content and face validity were calculated. Also, the Cronbach's alpha and test-retest were computed. Of all participants, 54.5% were male and aged 30 to 65 years old (69%). The Scale Content Validity Index was equal to 0.95. The Intra class Correlation Coefficient (ICC) was computed as 0.86, 0.88, and 0.87 for the core, optional, and total components, respectively. Furthermore, the Cronbach's alpha coefficient of 0.85 was obtained overall. This tool was highly valid and reliable for assessing risky behaviors among the Iranian general population.

Compassion and equity-focused clinical genomics training for health professional learners.

There remains an urgent need for expanded genomics training in undergraduate medical education, especially as genetic and genomic assessments become increasingly important in primary care and routine clinical practice across specialties. Physician trainees continue to report feeling poorly prepared to provide effective consultation or interpretation of genomic test results. Here we report on the development, pilot implementation, and evaluation of an elective offering for pre-clinical medical students called the Sanford Precision Health Scholars Immersive Learning Experience (PHS), which was designed leveraging genetic counseling expertise as one means to address this need. This 9-week course, piloted in Fall 2021 at UC San Diego, afforded students the opportunity to build technical skills and competencies in clinical genomics while identifying, addressing, and engaging with pervasive health disparities in genomics. Interactive exercises focused students' learning on strategies for empathic and compassionate patient interactions while supporting the application of concepts and knowledge to future practice. Upon completion of the course, participants reported increases in confidence related to skills required for clinical genomics practice. Drawing on learnings from this pilot implementation, recommendations for refining the program include deepening pedagogical engagement with ethical issues, expanding the offering to trainees across health professions, including pharmacy students, and incorporating an optional experiential learning component. Educational offerings, like PHS, that are designed with the input of genetic counseling expertise may ease pressures on the genetic counseling profession by building a more genomic-literate healthcare workforce that can better support efforts to expand access for patients.

Leveraging machine learning for taxonomic classification of emerging astroviruses.

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.

Personal goal setting eHealth component associated with improved weight loss at 6 months: A mixed methods secondary analysis.

Goal setting is a behavior change technique associated with improved change in outcomes. Digital (eHealth, mHealth) behavior change interventions often prescribe all goals with no opportunity for participants to create and track their own; thus, little is known about the types of goals participants create for themselves and their impacts on behavioral outcomes. This analysis describes the goals created by participants using an optional personal goal-setting component and evaluates the association between participant goal creation and weight loss in an eHealth adult weight loss intervention. This represents a mixed methods QUANT-qual design to understand the types of goals users create for themselves and their impacts on behavior change outcomes. Qualitative codes were applied for the topic, behavior/outcome focus, adherence to SMART criteria, and repetition with count summaries. Quantitative analyses applied regression modeling to determine if the number of goals set was associated with the 6-month weight change, controlling for covariates. Participants (n = 363) set an average of 23.4 goals (SD = 22.7) over 6 months. Those who reached at least 5% weight loss set significantly more goals than those who lost between 1% and 4.99% or who lost <1% or gained weight (p's < 0.0001). Setting more personal goals was associated with significant weight loss reduction at 6 months, controlling for covariates (p's < 0.05). Greater use of a personal goal-setting feature was associated with improved weight loss outcomes among active users. This can be a low-investment addition to digital behavior change interventions to contribute to improved outcomes.

Automated educational process support system with dynamic formation of optional components

Automated educational process support system with dynamic formation of optional components

ЭКОНОМИЧЕСКАЯ СУЩНОСТЬ МУЛЬТИГОЛОСУЮЩИХ АКЦИЙ И ВОЗМОЖНОСТЬ ИХ ИМПЛЕМЕНТАЦИИ НА РОССИЙСКОМ ФИНАНСОВОМ РЫНКЕ

Entrepreneurs often encounter challenges related to capital dilution and diminished control over strategic development when forming equity capital. The author proposes the adoption of multiple voting shares as a potential solution to this problem. It has been disclosed that a joint-stock company can issue multiple voting shares at various stages of its life cycle. The circulation of these shares is limited due to their conversion into ordinary shares during purchase and sale transactions, or in case of a specified duration of circulation prohibition. The implementation of a voting multiplier has been announced; clarifying that possessing multiple voting shares does not entitle the owner to extra dividend payments. Conversely, multiple voting shares are transformed into common shares at a one-to-one ratio determined by market value. The result of the study of the economic essence of multiple voting shares was the identification and analysis of the option component of this security. Therefore, there exists the potential to adopt an innovative method of generating financing opportunities for the company by introducing multiple voting shares that encompass the advantages of ordinary shares and a derivative financial instrument. Given the current state of the Russian financial market, where multiple voting shares are exclusively accessible to international companies, the author has developed proposals to modify the existing Russian legislation. These modifications will enable the standardization of equity formation practices among Russian joint-stock companies, while also enhancing the investment appeal of the Russian market for investors from allied states, where the trading of multiple voting shares is permitted.

EXAMINING THE GERIATRIC SCHOLARS PROGRAM: IMPACT ON CLINICAL PRACTICE AMONG VA PSYCHOLOGISTS

Abstract Since 2014, the Geriatric Scholars Program – Psychology Track (GSP-P) has offered VA post-licensure psychologists with little-to-no prior geropsychology training an opportunity to enhance their knowledge and skills to better serve rural-dwelling older Veterans. This is achieved through an intensive course on geropsychology competencies, completion of a geriatric mental health focused quality improvement project and optional advanced learning components. A survey of GSP-P alumni was conducted to assess the long-term impact of participation on clinical practice with older Veterans and overall job satisfaction. Out of 148 alumni, 40 (27%) responded to the survey. Most alumni reported working within the home based primary care setting (45%), with the others working in a range of inpatient and outpatient settings. Utilizing a retrospective post-pre design, practice change was evaluated using a measure comprised of 12 items related to clinical practices/behaviors with older Veterans (e.g., “Incorporated a caregiver/family member in the assessment of an older Veteran.”) both before and after participating in the GSP-P. A paired samples t-test, t(29) = -2.91, p = .007, demonstrated a significant difference in clinical practice pre to post engagement with GSP-P, with participants reporting more frequent age-adapted clinical practices/behaviors post GSP-P participation. Sensitivity analyses showed a moderate effect size d = -.53 (95% CI: -.91, -.144). Qualitative responses reflected alumni growth in confidence and satisfaction in working with older Veterans, as well as more utilization of age-adapted evidenced based assessment and intervention. Overall, this evaluation shows that targeted training can improve psychologists’ clinical practice with older Veterans.

Reflected Reality

We propose Reflected Reality: a new dimension for augmented reality that expands the augmented physical space into mirror reflections. By synchronously tracking the physical space in front of the mirror and the reflection behind it using an AR headset and an optional smart mirror component, reflected reality enables novel AR interactions that allow users to use their physical and reflected bodies to find and interact with virtual objects. We propose a design space for AR interaction with mirror reflections, and instantiate it using a prototype system featuring a HoloLens 2 and a smart mirror. We explore the design space along the following dimensions: the user's perspective of input, the spatial frame of reference, and the direction of the mirror space relative to the physical space. Using our prototype, we visualise a use case scenario that traverses the design space to demonstrate its interaction affordances in a practical context. To understand how users perceive the intuitiveness and ease of reflected reality interaction, we conducted an exploratory and a formal user evaluation studies to characterise user performance of AR interaction tasks in reflected reality. We discuss the unique interaction affordances that reflected reality offers, and outline possibilities of its future applications.