OBJECTIVE:The objective was to combine a set of linguistic analyses for efficiently analyzing large amounts of qualitative data from student evaluations of experiential sites and preceptors for quality assurance, evaluate their accuracy in coding these evaluations compared with manual processes, and assess the correlation between quantitative and qualitative sections of student evaluations. METHODS:A Python script was written to analyze comments from the qualitative sections of deidentified student evaluations of their preceptor and site. Each comment was analyzed using lexical, sentiment, and semantic analyses and given an aggregate rating, ranging from very negative to very positive. The script was iteratively refined using randomly selected samples of evaluations, subsequently tested to verify accuracy, and then used to analyze a cohort of evaluations. The correlation between the quantitative and qualitative sections was assessed to determine the strength of the relationship between the 2 sections. RESULTS:After refinements to the script, 93% of the qualitative sections of evaluations showed the correct valence (eg, positive, neutral, negative), as measured against agreed-upon experiential faculty ratings. Of the 7% coded incorrectly, 2% were programmatically relevant. The correlation between quantitative and qualitative sections was weak but significant. CONCLUSION:The linguistic analyses were able to identify sites potentially requiring further review, holding promise for alleviating burden on experiential staff by limiting the quantity of qualitative data to manually review. Correlation results suggested that quantitative data were not sufficient to capture potential concerns. Further research is necessary to evaluate integration into real-time experiential education workflows.

BACKGROUND:We are developing a cloud-based neurohealth data platform to aggregate and store data from large, complementary historical and ongoing studies focused on traumatic brain injury (TBI), a condition shaped by complex interactions among biological, environmental and contextual factors, and to enable secure, flexible querying across studies. These multifaceted datasets span clinical, physiological, behavioral, and environmental domains, mirroring the complexity of comparable multisite efforts such as the exposome moonshot. OBJECTIVE:Define a scalable and interoperable framework for organizing and harmonizing multifaceted data derived from different sources, purposes, and time scales, supporting precision neurohealth research and translational applications. METHODS:We designed and have begun implementing the Precision Health for Enhanced Neuro-Operational Modeling (PHENOM) platform, using a hybrid lakehouse architecture deployed on the public cloud. PHENOM employs automated extract-transform-load pipelines, metadata templates, and standardized biomedical terminologies to support FAIR-compliant data integration and cross-domain interoperability. We have mapped our findings to our datasets of interest in traumatic brain injury (TBI) and to the knowledge being generated for moonshot exposome efforts. RESULTS:The PHENOM framework organizes data into four general "bins": (1) One-time capture, (2) regular or continual lab or site visits, (3) personalized electronic health records, and (4) collective measures. The architecture enables ingesting multimodal data and supports AI-driven analytic tools and digital twin modeling through public cloud services. Initial deployment has demonstrated automated harmonization across multiple TBI studies and scalable query performance across datasets. SIGNIFICANCE:Scientists supported through large research programs, such as those funded by government initiatives or consortia, are increasingly encouraged (or required) to share their diverse data in public repositories in FAIR format. The proposed framework provides a flexible, cloud-based platform that meets these requirements by enabling the harmonization and secure sharing of multifaceted data across studies. Designed for scalability and interoperability with established data models, the platform supports both current research needs and a flexible structure responsive to those needs for use across future large-scale precision health initiatives. IMPACT STATEMENT:This Perspective proposes a cloud framework for structuring multifaceted data derived from complementary or consortia-related studies using four general "bins": (1) One-time capture, (2) regular or continual lab or site visits, (3) personalized health records, and (4) collective measures. The proposed framework offers a flexible structure for use across many existing research programs that are increasingly encouraged (or required) to share their diverse data in public repositories in FAIR format.

Drug development approaches increasingly harness computational modeling to predict drug behavior. These in silico approaches, collectively termed “pharmacometrics”, have significant value in deriving biological meaning from the analysis of pooled drug concentration vs. time (CvT) datasets. However, the field lacks standardization for pharmacokinetic data description, requiring expert annotation to enable aggregate mining and sharing. These limitations impede data sharing and preservation as mandated by current NIH policies. To this end, we propose a minimum information standard for pharmacokinetic studies composed of three categories ( Intervention , System , and Concentration ). We implement this standard in the development of a web-based database: the HIV Pharmacology Data Repository (HIV PDR). We describe our technical approach for creating the HIV PDR, the protocols we established for standardized data deposition, and the current content of the database. We also demonstrate the utility of the HIV PDR for pharmacometrics research through computational modeling of CvT data extracted from this new database. Based on these efforts, we propose the HIV PDR as a standard to preserve and share pharmacokinetic data generated through preclinical and clinical HIV research.

Pharmacy education faces significant challenges, particularly in keeping up with the rapidly evolving health care landscape due to the expansion of information and technology. The pressure to "teach everything" risks curriculum overload and detracts from defining core knowledge essential for pharmacists. This commentary advocates for a comprehensive definition of pharmacist knowledge as a framework to reorganize pharmacy education, aligning with knowledge domains outlined by the Organization for Economic Cooperation and Development. It proposes the Baumkuchen Layer Model, a metaphor inspired by the German layer cake, to integrate academic discipline classification with a hierarchical knowledge structure. This approach ensures a solid foundation for students, enabling them to engage with increasingly complex concepts, thus preparing them to navigate modern health care complexities, contribute to patient care, and support public health initiatives.

Background:Adolescent hospitalization for suicide-related thoughts and behaviors has increased over the past decade, with few interventions shown to improve outcomes post-discharge. Given the majority of adolescents return to school following a suicide-related crisis, we developed and pilot-tested Practice Experiences for School Reintegration (PrESR)™, a virtual reality (VR) intervention designed to teach therapeutic skills to hospitalized adolescents and allowing them to practice using these skills in stressful situations set in school settings that they would be expected to experience following discharge. Objective:This pilot optimization trial examined the feasibility, acceptability, and safety of PrESR for augmenting standard inpatient care with adolescents hospitalized for suicide-related crises. Method:Using a Multiphase Optimization Strategy (MOST) framework, we recruited adolescents hospitalized for suicide-related thoughts and behaviors (n=42) to be randomized into one of eight conditions testing three different VR-enhanced skill lessons and practice sessions: affect regulation, cognitive restructuring, and problem-solving. Results:Research clinicians delivered individual sessions of PrESR with a high degree of fidelity (93-100%), but patients were often discharged before completing more than one skill. Of the participants who participated in the intervention, the majority agreed or strongly agreed with statements endorsing PrESR and its components as important, appropriate, and easy to use, largely supporting acceptability. Participants also provided feedback for improvement, informing slight changes to the next version of the intervention. More generally, PrESR did not appear to result in significant safety concerns based self-reported ratings of subjective distress and pre/post measures of physical symptoms related to cybersickness. Conclusion:Findings support the feasibility of delivering a brief version of PrESR and preliminary acceptability for the intervention. VR technology and content show promising potential for adolescent suicide prevention and mental health support. Future research should examine PrESR for improving patient outcomes and preventing suicide.

(1) Background: As digital health technology evolves, the role of accurate medical-gloved hand tracking is becoming more important for the assessment and training of practitioners to reduce procedural errors in clinical settings. (2) Method: This study utilized computer vision for hand pose estimation to model skeletal hand movements during in situ aseptic drug compounding procedures. High-definition video cameras recorded hand movements while practitioners wore medical gloves of different colors. Hand poses were manually annotated, and machine learning models were developed and trained using the DeepLabCut interface via an 80/20 training/testing split. (3) Results: The developed model achieved an average root mean square error (RMSE) of 5.89 pixels across the training data set and 10.06 pixels across the test set. When excluding keypoints with a confidence value below 60%, the test set RMSE improved to 7.48 pixels, reflecting high accuracy in hand pose tracking. (4) Conclusions: The developed hand pose estimation model effectively tracks hand movements across both controlled and in situ drug compounding contexts, offering a first-of-its-kind medical glove hand tracking method. This model holds potential for enhancing clinical training and ensuring procedural safety, particularly in tasks requiring high precision such as drug compounding.

Extended reality (XR) simulations are becoming increasingly common in educational settings, particularly in medical education. Advancing XR devices to enhance these simulations is a booming field of research. This study seeks to understand the value of a novel, non-wearable mixed reality (MR) display during interactions with a simulated holographic patient, specifically in taking a medical history. Twenty-one first-year medical students at the University of North Carolina at Chapel Hill participated in the virtual patient (VP) simulations. On a five-point Likert scale, students overwhelmingly agreed with the statement that the simulations helped ensure they were progressing along learning objectives related to taking a patient history. However, they found that, at present, the simulations can only partially correct mistakes or provide clear feedback. This finding demonstrates that the novel hardware solution can help students engage in the activity, but the underlying software may need adjustment to attain sufficient pedagogical validity.

This study applied qualitative methods and a user design approach to develop and iteratively refine a model for a virtual reality intervention designed to supplement standard inpatient treatment for adolescents hospitalized for suicide-related crises: the practice experiences for school reintegration (PrESR). The PrESR model allows patients to practice therapeutic skills within an immersive school environment to increase skill knowledge and skill use and to improve school reintegration. Adolescents previously hospitalized for suicide-related thoughts and behaviors (n = 13), hospital professionals with experience providing supports to hospitalized adolescents (n = 7), and school professionals with experience supporting adolescents with suicide-related risks (n = 12) completed focus group and/or one-on-one interviews to inform the development of the PrESR model. Transcribed interviews were analyzed using content analysis, and structured feedback was analyzed by calculating frequencies. Participating adolescents were between the ages of 13 and 18, identifying their race as White (61%), Asian (7.7%), American Indian and Black (7.7%), or Black (7.7%; note that 15.4% preferred not to answer) and their ethnicity as Hispanic (23%) or non-Hispanic (77%). Adolescents identified their gender as girl or woman (46%), boy or man (38%), or "some other way" (15%). A majority of adolescent and professional participants endorsed the PrESR as holding the potential to promote skill learning. Feedback addressed improvements to scenarios and skills; safety concerns, constraints to consider, and barriers to implementation; and information to include in the treatment manual. Findings also informed the types of difficulties adolescents face in schools and the potential feasibility of a virtual reality intervention to enhance standard inpatient care of adolescents hospitalized for suicide-related crises.

Increasingly realistic virtual environments incorporating virtual characters have been used to train or assess actual behavior, such as of people at risk, and identify reasons to remediate or intervene. Technology has improved so rapidly that today's capabilities to create situations to focus training and intervention outshine past efforts. To name just a few current examples, tools like Unreal's MetaHuman Creator for creating characters, Midjourney for creating environments, OpenAI's ChatGPT for scripting, and GIFT for tutoring have enormous potential, as these tools promise to reduce simulation costs and increase realism. This paper, in contrast, discusses some movement in the other direction: Recent efforts suggest that increased realism may not always have resulting cost- benefit for training and assessment. Lessons learned and recommendations are presented to guide future developers.

The primary objective of this study was to build and test a virtual reality (VR) intervention that teaches adolescents hospitalized for suicide-related risk therapeutic skills and provides them opportunities to practice using these skills in immersive VR scenarios. The Practice Experiences for School Reintegration (PrESR) is facilitated by a clinician to help hospitalized adolescents prepare for their return to school. Our model was informed by input from adolescents with lived experience, school professionals, and hospital professionals, and the intervention was built and iteratively refined based on feedback from a community sample of adolescents (n=6), adolescents previously or currently hospitalized for suicide- related thoughts or behaviors (n=13), and hospital professionals (n=8). This paper describes the iterative development and refinement process leading to the final PrESR intervention, which is currently being tested in a pilot optimization trial.

OBJECTIVE:This study aimed to explore faculty engagement with qualitative comments from course evaluations.METHODS:Course faculty from the University of North Carolina Eshelman School of Pharmacy were recruited via email to participate in a 30-minute interview session. Previous course evaluation comments were adapted to create a de-identified mock evaluation. Six interviews were conducted via Zoom, consisting of a think-aloud protocol based on the mock course evaluation followed by a cognitive interview focused on goals and current utilization of comments, and common patterns and issues sought by faculty. Interview transcripts were manually cleaned and de-identified. Transcripts were inductively coded by 1 researcher using MAXQDA.RESULTS:Three overarching themes were identified: general faculty process for reviewing comments (ie, how faculty perceive and analyze comments), comments utilization for course change (ie, how faculty utilize comments in making course changes), and faculty analysis strategy (ie, faculty approach to locating common patterns in evaluation comments). The most common subthemes included usefulness of comments, methods for tracking comment patterns, and challenges with the large number of comments each semester.CONCLUSION:Faculty provided useful insight and feedback regarding the current state of the course evaluation process that can be used to improve the structure, organization, and utilization of course evaluations by faculty. These findings could inform the creation of the course evaluation comment automated analysis program in the next stage of an ongoing project.

Objective: The purpose of this paper is to describe a sentiment analysis program that aids in identifying pharmacy students at risk for progression issues by automatically scoring preceptor comments as positive or negative. Methods: An R-based program to analyze advanced pharmacy practice experiences and introductory pharmacy practice experiences midpoint evaluation of preceptor comments was piloted in phase 1 by comparing the sentiment analysis algorithm results to human coding. The algorithm was refined in phase 2. In phase 3, the validation phase, the final sentiment analysis algorithm analyzed all midpoint student evaluations (n = 1560). Sentiment scores were generated for each preceptor comment, and correlations were performed between sentiment scores and the quantitative scoring provided on the assessment. Results: In phase 1, agreement between faculty coders and sentiment analysis was 96%, and in phase 2, agreement between the final codes and sentiment analysis was 92.4% once keywords were added to the sentiment dictionary. In phase 3, a total of 3919 comments from 1560 evaluations were analyzed, and overall, the sentiment analysis results aligned with the quantitative data. Conclusion: This sentiment analysis algorithm was accurate in capturing positive and negative comments corresponding to pharmacy student performance. Given the accuracy of this preliminary validation for flagging preceptor comments, there are numerous implications when considering the use of sentiment analysis in pharmacy education. Using a sentiment analysis program minimizes the number of qualitative preceptor comments needing review by experiential faculty, as this program can aid in identifying students at risk of progression issues.

Clinical therapeutics are becoming increasingly reliant on data science and computational data analytical tools to predict drug behavior in unobserved conditions or populations. These in silico approaches, collectively termed “pharmacometrics”, derive biological meaning from the analysis of pooled drug concentration vs. time (CvT) datasets. However, the field lacks standardization for pharmacokinetic (PK) data description and sharing, instead requiring deep dives into the literature and expert data annotation for aggregate data mining. Here we introduce a minimum information sharing standard for PK studies composed of three categories (Intervention, System, and Effects) and implement this standard in the development of a web-based PK database: the HIV Pharmacology Data Repository (PDR). We demonstrate the utility of the HIV PDR by computational modeling of aggregated and curated CvT data extracted from the HIV PDR.

BACKGROUND:Complex contributions of environment to health are intimately connected to human behavior. Modeling of human behaviors and their influences helps inform important policy decisions related to critical environmental and public health challenges. A typical approach to human behavior modeling involves generating daily schedules based on time-activity patterns of individual humans, simulating 'agents' with these schedules, and interpreting patterns of life that emerge from the simulation to inform a research question. Current behavior modeling, however, rarely incorporates the context that surrounds individuals' truly broad scope of activities and influences on those activities.OBJECTIVES:We describe in detail a range of elements involved in generating time-activity patterns and connect work in the social science field of behavior modeling with applications in exposure science and environmental health. We propose a framework for behavior modeling that takes a systems approach and considers the broad scope of activities and influences required to simulate more representative patterns of life and thus improve modeling that underlies understanding of environmental contributions to health and associated decisions to promote and protect public health.METHODS:We describe an agent-based modeling approach reliant on generating a population's schedules, filtering the schedules, simulating behavior using the schedules, analyzing the emergent patterns, and interrogating results that leverages general empirical information in a systems context to inform fit-for-purpose action.DISCUSSION:We propose a centralized and standardized program to codify behavior information and generate population schedules that researchers can select from to simulate human behavior and holistically characterize human-environment interactions for a variety of public health applications.

This paper presents a study of the interaction between healthcare providers (HCPs) and older patients and their caregivers. The paper first presents results from a rapid review and narrative synthesis using PubMed and Google Scholar of HCP/patient/caregiver interactions involving older patients; these results then informed the design of a survey administered to HCPs and caregivers using a range of scenarios and their ratings of appropriateness of different responses, to explore where expectations align or differ between HCPs and caregivers. In analyzing ratings, the research found HCPs and caregivers generally approach the older adult encounter with similar expectations, but differences for specific situations are informative. HCPs appear to better recognize when there is a need to show empathy, as when a patient is frustrated or anxious. HCPs, overall, offer more calming responses, especially in embarrassing, upsetting, or worrying situations. For older patients of advanced age, HCPs value engagement with patients more than caregivers, but HCPs are more aligned with caregivers in their ratings of how to engage caregivers. Compared to caregivers, HCPs focus more on simplifying the description of treatment rather than using thorough explanations when a patient expresses hesitancy or avoidance. The results from this work suggest that having a fuller understanding of the different participants' expectations may improve communication and identify potential pitfalls. A better understanding may also lead to changes in how students in the healthcare fields are trained; having better insight into this relationship will prepare them for interacting with older patients while addressing the needs of caregivers.

Nutrition apps have been designed to assist users in determining the nutritional value of their food in alliance with their dietary restrictions and/or health. However, many of these apps do not incorporate or recognize the cuisine of the diverse communities that they serve. In this work we assessed several of the most popular free apps and compared with the most popular dishes in Latin America, Europe, India, East Asia, West Africa, and the American South to determine the rate of recognition of culturally diverse cuisines in popular nutrition apps. We built a consensus database of over 150 culturally popular foods via a web scraper for foods from Latin American, Europe, India, East Asia, West Africa, and the American South. Researched & logged popular dishes from International cuisines with their corresponding nutritional data. Each food item was run against Calorie Mama, Lose It, My Fitness Pal and My Plate to determine the recognition and availability of the nutritional value. European and Indian dishes were better recognized (greater than 70%) across all apps than West African and Latin American dishes. Of the dishes that were recognizable, most were due to manual entry and lacked some of the nutritional information, making the apps less useful. We note a bias among popular nutrition apps in not taking into account cuisines, from both international and regional American communities, leaving many communities underserved and contributing to health inequalities gap. N/A.

(1) Background: This proof-of-concept study assessed an interactive web-based tool simulating three challenging non-academic learning situations-student professionalism, cross-cultural interactions, and student well-being-as a means of preceptor development. (2) Methods: Three scripts focused on professionalism, cross-cultural interactions, and student well-being were developed and implemented using a commercial narrative tool with branching dialog. Delivered online, this tool presented each challenge to participants. Participants had up to four response options at each turn of the conversation; the choice of response influenced the subsequent conversation, including coaching provided at the resolution of the situation. Participants were invited to complete pre-activity, immediate post-activity, and one-month follow-up questionnaires to assess satisfaction, self-efficacy, engagement, and knowledge change with the tool. Knowledge was assessed through situational judgment tests (SJTs). (3) Results: Thirty-two pharmacist preceptors participated. The frequency of participants reflecting on challenging learning situations increased significantly one-month post-simulation. Participants affirmatively responded that the tool was time-efficient, represented similar challenges they encountered in precepting, was easily navigable, and resulted in learning. Self-efficacy with skills in managing challenging learning situations increased significantly immediately post-simulation and at a one-month follow-up. Knowledge as measured through SJTs was not significantly changed. (4) Conclusions: Preceptors found an interactive narrative simulation a relevant, time-efficient approach for preceptor development for challenging non-academic learning situations. Post-simulation, preceptors more frequently reflected on challenging learning situations, implying behavior change. Self-efficacy and self-report of knowledge increased. Future research is needed regarding knowledge assessments.

Laboratory safety has received heightened attention due to a series ofdevastatingly tragic accidents in both academic and nonacademic settings.Consequently, chemistry departments at various academic institutions now offersome form of formal training in laboratory safety for entering graduate students.Although the extent of this training varies widely among institutions, it typicallyincludes an online assessment and/or minimal in-person classroom instruction.However, a significant gap exists between a lecture hall setting and the complexenvironment that comprises an advanced research laboratory. We've adapted thetechnological advances in virtual and augmented reality to bridge this gap. A set of360 degrees virtual reality lab experiences, highlighting safety infractions, have been createdfor a variety of subdiscipline-distinct (medicinal, organic, inorganic, physical, drugscreening) laboratory settings. Notable features include the accurate depiction of thevisual complexity associated with research settings, the opportunity for the trainee toexplore multiple laboratories in a self-paced fashion, and immediate feedback withrespect to the identification of safety hazards. The VR Lab Safety modules were very well received byfirst year graduate students,with greater than 85% of the respondents describing the VR experience as engaging and memorable, as a good supplement to safetyreading material, and as providing real world examples that are otherwise difficult to visualize