Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=keywords
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=keywords", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=keywords", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=keywords", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=keywords" }, "data": [ { "type": "Grant", "id": "5709", "attributes": { "award_id": "3D43TW006589-17S1", "title": "Integration of Translational and Implementation Research Training on Leishmaniasis and other Vector-borne and Emerging Infectious Diseases", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Fogarty International Center (FIC)" ], "program_reference_codes": [], "program_officials": [ { "id": 19693, "first_name": "Barbara J", "last_name": "Sina", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2003-09-01", "end_date": "2025-03-31", "award_amount": 75000, "principal_investigator": { "id": 19694, "first_name": "Nancy", "last_name": "Gore Saravia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 19695, "first_name": "CHRISTIAN", "last_name": "TSCHUDI", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1066, "ror": "", "name": "CENTRO INTERNACIONAL (CIDEIM)", "address": "", "city": "", "state": "", "zip": "", "country": "COLOMBIA", "approved": true }, "abstract": "PROGRAM SUMMARY Our Global Infectious Disease research training program has focused on mitigating leishmaniasis and other vector-borne and emerging infectious diseases in Colombia and the Latin American Region through basic, clinical, and translational research and training. The challenges of prevention and control of infectious diseases in Colombia and other low and middle income countries, require innovation not only through discovery research but also in the delivery of new interventions in diverse contexts. The overall goal of this renewal application is to strengthen the investigator base in infectious disease research in CIDEIM and other Colombian institutions and their research and training capacity to enable implementation of the results of research on these diseases into practice. This will be achieved through: a) early research career development of eight New Investigators and Junior Faculty as Global Infectious Disease (GID) Scholars and their establishment as investigators in CIDEIM and other Colombian institutions, b) continuation of mentored research and comprehensive research training of 4 Doctoral and 6 Clinical Research/MSc trainees and, c) by integrating the principles of implementation science into both our GlD research training curriculum and mentored trainee projects across the spectrum of basic to applied, biomedical, clinical and public health research. In alliance with Yale University, the committed participation of national post-graduate programs in biomedical and health sciences, together with a strong platform of funded research at CIDEIM and the ongoing research of GID program faculty, the renewal of this program will continue to create synergies with national and regional post-graduate programs in biomedical and health sciences through an integrative portfolio of post- graduate elective web-based interinstitutional courses and seminars, workshops on leadership, mentorship, and good research practices, and strategic use of communication technologies.", "keywords": [ "Basic Science", "Clinical", "Clinical Research", "Colombia", "Colombian", "Communicable Diseases", "Communication", "Disease", "Educational Curriculum", "Educational workshop", "Emerging Communicable Diseases", "Faculty", "Funding", "Goals", "Health Sciences", "Infectious Diseases Research", "Institution", "Intervention", "Latin American", "Leadership", "Leishmaniasis", "Mentors", "Mentorship", "Online Systems", "Prevention", "Research", "Research Personnel", "Research Training", "Technology", "Training Programs", "Translational Research", "Universities", "base", "career development", "implementation research", "implementation science", "innovation", "low and middle-income countries", "programs", "public health research", "synergism", "vector-borne" ], "approved": true } }, { "type": "Grant", "id": "10495", "attributes": { "award_id": "75N93022C00047-0-9999-1", "title": "REAL-TIME SURVEILLANCE OF VACCINE MISINFORMATION FROM SOCIAL MEDIA PLATFORMS", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2022-08-08", "end_date": "2023-08-07", "award_amount": 300000, "principal_investigator": { "id": 26502, "first_name": "JINGCHENG", "last_name": "DU", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1763, "ror": "", "name": "MELAX TECHNOLOGIES, INC.", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "To develop digital tools to identify and combat malicious digital bots that spread misinformation about infectious disease treatments and vaccines, including COVID-19 vaccines.", "keywords": [ "Basic Science", "COVID-19 vaccine", "Coronavirus", "Misinformation", "Severe Acute Respiratory Syndrome", "Time", "Vaccines", "combat", "digital", "infectious disease treatment", "social media", "tool" ], "approved": true } }, { "type": "Grant", "id": "6282", "attributes": { "award_id": "3R01GM123007-03S1", "title": "Real-time syndromic surveillance and modeling to inform decision-making for COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 21227, "first_name": "Han", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2017-09-16", "end_date": "2022-06-30", "award_amount": 30885, "principal_investigator": { "id": 21228, "first_name": "Shweta", "last_name": "Bansal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "The rapid spread of COVID-19 around the United States has created an unprecedented public health emergency. It is now clearly appreciated that smart policy responses to this pandemic require the utilization of reliable, validated transmission models. Models are critical both in terms of forecasting the spatio-temporal spread of the virus, but also in permitting a rational comparison of alternative non-pharmaceutical intervention strategies. To fill this urgent surveillance gap and inform policy decisions, we propose to model the spatio-temporal dynamics of COVID-19 in the US from novel streams of real-time healthcare data. Our combination of sophisticated computational and statistical models, together with unique high-resolution data will allow a careful characterization of the burden of COVID-19 beyond tested cases, discriminate among alternative mitigation policies, and quantify the geographic variation in population immunity as we prepare for the Fall wave.", "keywords": [ "Bayesian Modeling", "COVID-19", "COVID-19 pandemic", "Cessation of life", "Clinical", "Computer Models", "Country", "Data", "Decision Making", "Development", "Epidemiology", "Future", "Geographic Distribution", "Grain", "Health", "Healthcare", "Immune", "Immunity", "Individual", "Intervention", "Kinetics", "Maps", "Modeling", "Nature", "Online Systems", "Play", "Policies", "Policy Maker", "Population", "Population Distributions", "Readiness", "Resolution", "Role", "Schools", "Series", "Serologic tests", "Social Distance", "Statistical Models", "Stream", "Surveillance Modeling", "Symptoms", "Testing", "Time", "United States", "Validation", "Virus", "burden of illness", "dashboard", "data streams", "epidemiological model", "falls", "geographic difference", "novel", "pandemic disease", "public health emergency", "response", "spatiotemporal", "surveillance data", "syndromic surveillance", "transmission process" ], "approved": true } }, { "type": "Grant", "id": "5201", "attributes": { "award_id": "1R01AG074710-01", "title": "The Care Ecosystem Response to COVID-19: Accelerating Research on Dementia Care that Meets the Needs of Caregivers and Persons with Dementia during COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Aging (NIA)" ], "program_reference_codes": [], "program_officials": [ { "id": 18415, "first_name": "Elena", "last_name": "Fazio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-15", "end_date": "2025-05-31", "award_amount": 2908756, "principal_investigator": { "id": 18416, "first_name": "Katherine Laurel", "last_name": "Possin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 768, "ror": "https://ror.org/043mz5j54", "name": "University of California, San Francisco", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Dementia causes substantial burdens for patients and caregivers, which have been exacerbated by the COVID- 19 pandemic. The current state of dementia care is inadequate to meet the needs of this growing, vulnerable population. Scalable, effective, and person-centered dementia care models that are aligned with value-based healthcare reforms are needed now. The Care Ecosystem is an accessible, remotely delivered team-based dementia care model, designed to add value for patients, providers and payers in complex organizational and reimbursement structures. Care is delivered via the phone and web by unlicensed Care Team Navigators, who are trained and supervised by a team of dementia specialists with nursing, social work, and pharmacy expertise. Care Protocols guide proactive, quality care that is documented in the electronic health record. The evidence base to date suggests that the Care Ecosystem improves outcomes important to people with dementia, caregivers, and payers when delivered in a controlled research environment, including reduced emergency department visits, higher quality of life for patients and lower caregiver depression. We propose a rapid pragmatic trial in 6 health systems serving geographically and culturally diverse populations. We will leverage technology, delivering care via the phone and web and using electronic health records to monitor quality improvements and evaluate outcomes while maximizing external validity. In Aim 1, we will use implementation science to identify the model adaptations, facilitators, and barriers to implementing and sustaining the Care Ecosystem during the COVID-19 pandemic. In Aim 2 we will use mixed methods to rigorously evaluate the effectiveness of the Care Ecosystem on outcomes important to patients, caregivers, healthcare providers, and health systems during the pandemic. In Aim 3, we will characterize the patient and caregiver factors associated with treatment benefit. This will include investigating effectiveness in underrepresented groups and elucidating unmet needs that will guide future development work. By simultaneously evaluating the real-world effectiveness and implementation strategies in diverse health systems, this project will bridge the science-practice gap in dementia care during an unprecedented time of heightened strain on family caregivers, healthcare providers and health systems. Furthermore, this work will pave the way for expanding access to high quality dementia care in the future, mitigating the negative impact of dementia on patients and their families across the nation.", "keywords": [ "Bed Occupancy", "Behavior", "Behavioral Symptoms", "COVID-19", "COVID-19 pandemic", "Caregiver Burden", "Caregiver support", "Caregiver well-being", "Caregivers", "Caring", "Clinical", "Communication", "Communities", "Community Health", "Complex", "Data", "Dementia", "Dementia caregivers", "Development", "Discipline of Nursing", "Ecosystem", "Effectiveness", "Electronic Health Record", "Emergency department visit", "Emergency medical service", "Enrollment", "Environment", "Evaluation", "Family", "Family Caregiver", "Fee-for-Service Plans", "Fright", "Future", "Geography", "Goals", "Health Care Reform", "Health Personnel", "Health system", "Healthcare Systems", "Home", "Hospitalization", "Infection", "Internet", "Intervention", "Interview", "Latinx", "Locales", "Medical", "Medicare claim", "Mental Depression", "Methods", "Minority Groups", "Modeling", "Monitor", "Outcome", "Participant", "Patients", "Persons", "Pharmaceutical Preparations", "Pharmacy facility", "Population Heterogeneity", "Primary Health Care", "Protocols documentation", "Provider", "Public Health", "Quality of Care", "Quality of life", "Randomized Clinical Trials", "Research", "Resources", "Risk", "Risk Reduction", "Rural", "Science", "Services", "Site", "Social Work", "Specialist", "Standardization", "Structure", "Supervision", "Supportive care", "Surveys", "Technology", "Telephone", "Time", "Training", "Underrepresented Populations", "Underserved Population", "Vulnerable Populations", "Work", "advanced dementia", "advanced disease", "barrier to care", "base", "caregiver depression", "cohort", "collaborative care", "comparison group", "cost", "dementia care", "effectiveness evaluation", "effectiveness study", "evidence base", "experience", "family burden", "health care model", "high risk", "hospital readmission", "implementation barriers", "implementation facilitators", "implementation science", "implementation strategy", "improved", "improved outcome", "model design", "pandemic disease", "patient population", "person centered", "pragmatic trial", "primary outcome", "programs", "racial and ethnic", "remote delivery", "remote health care", "response", "satisfaction", "secondary outcome", "social" ], "approved": true } }, { "type": "Grant", "id": "6920", "attributes": { "award_id": "2R35GM119770-06", "title": "Multi-cellular and multi-scale systems modeling to understand the dynamics of the human immune system in interdisciplinary applications", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 22759, "first_name": "DOROTHY", "last_name": "Beckett", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2016-09-01", "end_date": "2026-11-30", "award_amount": 371250, "principal_investigator": { "id": 16107, "first_name": "Tomas", "last_name": "Helikar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 298, "ror": "", "name": "University of Nebraska-Lincoln", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 978, "ror": "", "name": "UNIVERSITY OF NEBRASKA LINCOLN", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true }, "abstract": "The immune system is arguably the second most complex human system after the brain. Its proper response to foreign stimuli is governed by network-like interactions among various types of cells and cytokines as their communication mediators. The complexity at the inter-cellular level of the immune system is further exacerbated by the similarly complex biological and biochemical networks within each cell (metabolism, gene regulation, etc.) responsible for the dynamics and decision-making at the single-cell level. Such multiscale complexity makes it incredibly challenging to understand the complete etiology and pathology of immune-system-related diseases. My research program aims to identify how the immune system can be rewired en masse to elicit higher-order decision-making while still enabling the system to remain otherwise “healthy.” To this end, my research program is leveraging a highly interdisciplinary research team (computational and experimental immunologists, software engineers, and education researchers) and collaborators to build a Virtual Immune System -- a multi-scale, multi-approach computational framework to understand better the complex dynamical nature of the immune system, identify more accurate multi-dimensional biomarkers, and identify safe and effective treatments within a reasonable time and cost. In the next five years, in addition to expanding the Virtual Immune system, my program will continue to develop methods and technologies for data-driven model construction, visualization, computation, real-time simulations, and reproducibility to advance multi-scale modeling of the immune system and beyond. We will continue to decipher the dynamics of the immune system under various pathologies and the re-programmability of CD4+ T cells under the milieu of their microenvironments. My laboratory will continue to iteratively predict, validate, and refine predictions generated using the systems approaches and technologies. To do this, we will generate our multi-omics data to more precisely validate immune system behaviors and apply our findings to refine the computational approaches directly. My team will continue to build collaborations and deepen our existing relationships, including with translational partners to advance the impact of our systems work on drug discovery, the international team modeling COVID-19, and with virologists and immunologists to further validate our computational predictions experimentally.", "keywords": [ "Behavior", "Biochemical", "Biological", "Biological Markers", "Biological Models", "Brain", "CD4 Positive T Lymphocytes", "COVID-19", "Cells", "Cellular Metabolic Process", "Collaborations", "Communication", "Communities", "Complex", "Computer software", "Data", "Decision Making", "Disease", "Education", "Etiology", "Funding", "Gene Expression Regulation", "Human", "Immune", "Immune response", "Immune system", "Immunological Models", "Immunologist", "Interdisciplinary Study", "International", "Laboratories", "Mediator of activation protein", "Methods", "Modeling", "Multiomic Data", "Nature", "Pathology", "Pharmacotherapy", "Property", "Reproducibility", "Research", "Research Personnel", "Software Engineering", "Stimulus", "System", "Technology", "Time", "Visualization", "Work", "cell type", "computer framework", "cost", "cytokine", "design", "drug discovery", "effective therapy", "multi-scale modeling", "pathogen", "predictive modeling", "programs", "response", "simulation", "translational scientist", "virtual" ], "approved": true } }, { "type": "Grant", "id": "7298", "attributes": { "award_id": "5R35GM133439-02", "title": "Leveraging environmental drivers to predict vector-borne disease transmission", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 7693, "first_name": "Veerasamy", "last_name": "Ravichandran", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2019-09-01", "end_date": "2024-08-31", "award_amount": 654082, "principal_investigator": { "id": 7493, "first_name": "Erin", "last_name": "Mordecai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Erin Mordecai NIGMS R35 ESI MIRA Summary Leveraging environmental drivers to predict vector-borne disease transmission Vector-borne diseases are an increasingly urgent public health crisis worldwide. Traditional biomedical approaches such as vaccines and drugs alone will not sustainably control vector-borne diseases or prevent future emergence. More proactive, ecological approaches that discover and disrupt the environmental drivers of vector transmission are critical for understanding and sustainably controlling disease epidemics. Predicting infectious disease dynamics from ecological drivers like climate and land use is appealing because these drivers are readily observable and often predictable, and their impacts on disease transmission are supported by mechanistic hypotheses. However, vector-borne diseases, like other ecological systems, are nonlinear, complex, and dynamic, making prediction challenging in a stochastic and changing world. My research uses brings in techniques from quantitative ecology, statistics, mathematics, econometrics, and geography as well as newly available data sources to understand and predict vector-borne disease dynamics in response to global change. Our preliminary work has shown that climate and land use are powerful predictors of geographical and seasonal patterns of disease transmission. I now propose to extend this work to understand disease dynamics using cutting edge quantitative techniques and time series data. Specifically, we will investigate how climate, habitat, behavior, and immunity interact to determine disease dynamics over space and time for malaria, Zika, dengue, and other vector-borne pathogens, building a portfolio of evidence and predictive tools from multiple complementary quantitative approaches. These include fitting increasingly sophisticated dynamic models to time series data, applying empirical dynamic modeling to infer, rather than assume, mechanistic relationships with ecological drivers, and applying econometric panel analysis to remotely sensed and geographic data to evaluate evidence for bidirectional causation between disease and human land use activities. Recent decades have witnessed both unprecedented expansions in both vector-borne disease and technological and computational capacity. In response, vector-borne disease modeling research is rapidly accelerating, with the goal of improving prospective prediction and thereby opening opportunities for proactive control. By developing and testing new theory, this project will finally allow us to leverage environmental drivers of vector-borne disease to understand the mechanisms underlying complex disease dynamics, and to predict future disease risk in changing environments.", "keywords": [ "Behavior", "Climate", "Communicable Diseases", "Complex", "Data", "Data Sources", "Dengue", "Disease", "Disease model", "Ecology", "Ecosystem", "Environment", "Epidemic", "Etiology", "Future", "Geography", "Global Change", "Goals", "Habitats", "Human", "Immunity", "Malaria", "Mathematics", "Modeling", "National Institute of General Medical Sciences", "Pattern", "Pharmaceutical Preparations", "Predisposition", "Public Health", "Research", "Series", "System", "Techniques", "Temperature", "Testing", "Time", "Vaccines", "Vector-transmitted infectious disease", "Work", "ZIKA", "disease transmission", "disorder risk", "econometrics", "improved", "land use", "predictive tools", "prevent", "prospective", "remote sensing", "response", "statistics", "theories", "tool", "transmission process", "vector", "vector competence", "vector control", "vector transmission", "vector-borne pathogen" ], "approved": true } }, { "type": "Grant", "id": "7888", "attributes": { "award_id": "3U01GM132175-03S1", "title": "Keeping rural minority 'essential' workplaces open safely during the COVID-19 pandemic: The role of frequent point-of-care molecular workplace surveillance for miners", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22735, "first_name": "Michael A.", "last_name": "Sesma", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-14", "end_date": "2023-07-13", "award_amount": 701030, "principal_investigator": { "id": 23736, "first_name": "AKSHAY", "last_name": "SOOD", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 761, "ror": "", "name": "UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 761, "ror": "", "name": "UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR", "address": "", "city": "", "state": "NM", "zip": "", "country": "United States", "approved": true }, "abstract": "Although mentoring is critical to the success of investigators, the best practices for training and supporting mentors are not known. There is an urgent need to correct this gap in knowledge, particularly at institutions with large proportions of underrepresented minority (URM) faculty scientists. Our long-term goal is to create and scale interventions to enhance the skills of the diverse workforce to meet the nation's research needs. Our objective is to develop, implement, and evaluate theoretically grounded mentor development programs, mentor support networks, and customized institutional mentoring climate interventions. The rationale for our project is based upon the literature that conveys that mentors currently feel unprepared and unsupported in an institutional climate that is not aligned with their needs. We will accomplish our objective with the following Specific Aims: Aim 1: To compare the effectiveness of a combined online plus face-to-face research mentor development program vs. an online program alone, using a non-randomized quasi-experimental study. Hypothesis 1A: The combined mentor intervention results in greater and sustained improvement in mentor competency than the control intervention. Hypothesis 1B: The combined mentor intervention results in greater faculty mentee success in achieving milestones, including scholarly products, grants, and navigating critical career decisions. Hypothesis 1C: The combined mentor intervention results in greater number and quality of mentor-mentee behavioral interactions. Aim 2: To compare the effectiveness of a structured mentor support network, using a randomized controlled trial. Hypothesis 2A: As compared to mentors who do not participate, mentor support network participation results in a greater change in mentors' developmental network diversification and supportive characteristics. Hypothesis 2B: Mentors' developmental network diversification and supportive characteristic scores positively correlate with the faculty mentees' corresponding developmental network scores, subjective career success inventory scores, and research productivity. Hypothesis 2C: Content analysis will provide insights into contributions to changes in network characteristics of URM mentors and mentees and in career success and research productivity for URM mentees. Feasibility Aim 3: To determine the feasibility of developing, implementing, and evaluating customized institutional mentoring climate interventions. Hypothesis 3A: A faculty survey of mentoring climate and focus groups/interviews with institutional stakeholders can be used to identify institutional interventions of value related to mentoring structure, programs/activities, and/or policies/guidelines. Hypothesis 3B: Interventions that impact mentoring structure, programs/activities, and/or policies/guidelines improve the measured institutional mentoring climate in the study population. The completion of these Aims will establish the feasibility and effectiveness of mentor interventions, particularly among URM faculty at three southwestern institutions. This will allow evidence-based activities to replace trial-and-error approaches, expanding the scientific scope of the National Research Mentoring Network.", "keywords": [ "Behavioral", "COVID-19 pandemic", "Characteristics", "Climate", "Competence", "Custom", "Development", "Diverse Workforce", "Effectiveness", "Equipment and supply inventories", "Faculty", "Focus Groups", "Goals", "Grant", "Group Interviews", "Guidelines", "Institution", "Intervention", "Knowledge", "Literature", "Measures", "Mentors", "Molecular", "Policies", "Productivity", "Program Development", "Quasi-experiment", "Randomized Controlled Trials", "Research", "Research Personnel", "Role", "Rural Minority", "Scientist", "Structure", "Surveys", "Training Support", "Underrepresented Minority", "Workplace", "base", "career", "compare effectiveness", "evidence base", "improved", "insight", "organizational climate", "point of care", "programs", "skills", "study population", "success", "support network" ], "approved": true } }, { "type": "Grant", "id": "5876", "attributes": { "award_id": "3R01AI127203-05S1", "title": "BDD CIS: Big Data Driven Clinical Informatics & Surveillance - A Multimodal Database Focused Clinical, Community, & Multi-Omics Surveillance Plan for COVID19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 20120, "first_name": "Rosemary G", "last_name": "McKaig", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-12", "end_date": "2023-05-31", "award_amount": 626275, "principal_investigator": { "id": 20121, "first_name": "Xiaoming", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 20122, "first_name": "Bankole", "last_name": "Olatosi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 930, "ror": "", "name": "UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "With South Carolina’s population already being vulnerable to poor health as evidenced by poor national health rankings, challenging rural geography and health professional shortages, the impact of the novel Coronavirus Disease 2019 (COVID-19) will be long lasting in the state. Patient morbidity and mortality rates already continue to increase, with ongoing economic damage to health systems and businesses. The speed of transmission and geographical spread of COVID-19 across South Carolina and the United States is alarming, which combined with the novel nature of the disease justifies the need for accelerated research to combat this pandemic. As clinicians and frontline health workers battle to save lives, creating a data environment that accelerates research is key, and necessary to fight against the disease. This proposal will build the capacity for accelerated research and intelligence gathering by coalescing multiple state partners and leveraging relevant data for discoveries around COVID-19. To accomplish this, this proposal aims to (1) create a de-identified linked database system via a HIPAA compliant secure server to collate surveillance, clinical, multi-omics and geospatial data on both COVID-19 patients and health workers treating COVID-19 patients in South Carolina; (2) examine the natural history of COVID-19 including transmission dynamics, disease progression, and geospatial visualization; and (3) identify important predictors of short- and long-term clinical outcomes of COVID-19 patients in South Carolina using machine learning algorithms. These aims will be accomplished through collaborations with multiple state agencies and stakeholders relevant to COVID-19 and the creation of a secure HIPAA compliant database that allow for coalescing relevant data in a timely fashion, combined with leveraging of statewide integrated data warehouse capabilities.", "keywords": [ "Big Data", "Businesses", "COVID-19", "COVID-19 patient", "COVID-19 surveillance", "COVID-19 treatment", "Clinical", "Clinical Informatics", "Collaborations", "Communities", "Confidentiality of Patient Information", "Coupled", "Data", "Data Discovery", "Database Management Systems", "Databases", "Disease", "Disease Progression", "Disease Surveillance", "Disease susceptibility", "Economics", "Environment", "Geography", "Health", "Health Insurance Portability and Accountability Act", "Health Professional", "Health system", "Individual", "Intelligence", "Intervention", "Learning", "Link", "Monitor", "Morbidity - disease rate", "Multiple Partners", "Natural History", "Nature", "Outcome", "Patients", "Population", "Recovery", "Research", "Rural", "Secure", "South Carolina", "Speed", "Techniques", "Time", "United States", "Visualization", "big-data science", "combat", "data infrastructure", "data warehouse", "disability", "fight against", "health data", "innovation", "machine learning algorithm", "mortality", "multimodality", "multiple data sources", "multiple omics", "novel", "novel coronavirus", "pandemic disease", "patient privacy", "systems research", "transmission process", "virology" ], "approved": true } }, { "type": "Grant", "id": "9280", "attributes": { "award_id": "75N91020C00035-0-9999-1", "title": "DIGITAL HEALTH SOLUTIONS FOR COVID-19: CLEAR2GO", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-09-14", "end_date": "2020-11-27", "award_amount": 274880, "principal_investigator": { "id": 25020, "first_name": "ADARBAD", "last_name": "MASTER", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1816, "ror": "", "name": "ICRYPTO, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": null, "abstract": "The goal of this project is to develop a smartphone-based platform to provide irrefutable proof of testing, serologic, and vaccination status for individuals. The customized solution, called Clear2Go (C2G), maps a person’s vetted identity and biometrics to their phone and then cryptographically binds it with their COVID-19 test results or other personal health information. C2G is intended to provide individuals, employers, government agencies, and other organizations with a convenient solution to evaluate the risks associated with an individual’s return to normal work, travel, and public life activities. The aims of this project include creating the C2G solution, demonstrating that C2G can be integrated with the publicly available interface of a test laboratory portal, and conveying simulated COVID-19 test results into the C2G application. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.", "keywords": [ "Binding", "Biometry", "COVID-19", "Cellular Phone", "Custom", "Data", "Goals", "Government Agencies", "Health", "Individual", "Laboratories", "Life", "Maps", "Persons", "Risk", "Secure", "Serological", "Telephone", "Test Result", "Testing", "Travel", "United States National Institutes of Health", "Vaccination", "Work", "base", "cryptography", "data hub", "digital" ], "approved": true } }, { "type": "Grant", "id": "9279", "attributes": { "award_id": "75N91020C00035-P00002-9999-1", "title": "DIGITAL HEALTH SOLUTIONS FOR COVID-19: CLEAR2GO", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2020-09-14", "end_date": "2021-09-13", "award_amount": 1524610, "principal_investigator": { "id": 25020, "first_name": "ADARBAD", "last_name": "MASTER", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1816, "ror": "", "name": "ICRYPTO, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1816, "ror": "", "name": "ICRYPTO, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The goal of this project is to develop a smartphone-based platform to provide irrefutable proof of testing, serologic, and vaccination status for individuals. The customized solution, called Clear2Go (C2G), maps a person’s vetted identity and biometrics to their phone and then cryptographically binds it with their COVID-19 test results or other personal health information. C2G is intended to provide individuals, employers, government agencies, and other organizations with a convenient solution to evaluate the risks associated with an individual’s return to normal work, travel, and public life activities. The aims of this project include creating the C2G solution, demonstrating that C2G can be integrated with the publicly available interface of a test laboratory portal, and conveying simulated COVID-19 test results into the C2G application. Data collected under this project will be deidentified and securely transmitted to an NIH data hub.", "keywords": [ "Binding", "Biometry", "COVID-19", "COVID-19 test", "Cellular Phone", "Custom", "Data", "Goals", "Government Agencies", "Health", "Individual", "Laboratories", "Life", "Maps", "Persons", "Risk", "Secure", "Serology", "Telephone", "Test Result", "Testing", "Travel", "United States National Institutes of Health", "Vaccination", "Work", "base", "cryptography", "data hub", "digital health" ], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1392, "count": 13920 } } }