Faculty Bibliography

BACKGROUND: The New York University Health Sciences Library data services team had developed educational material for research data management and data visualization and had been offering classes at the request of departments, research groups, and training programs, but many members of the medical center were unaware of these library data services. There were also indications of data skills gaps in these subject areas and other data-related topics. CASE PRESENTATION: The data services team enlisted instructors from across the medical center with data expertise to teach in a series of classes hosted by the library. We hosted eight classes branded as a series called "Data Day to Day." Seven instructors from four units in the medical center, including the library, taught the classes. A multipronged outreach approach resulted in high turnout. Evaluations indicated that attendees were very satisfied with the instruction, would use the skills learned, and were interested in future classes. CONCLUSIONS: Data Day to Day met previously unaddressed data skills gaps. Collaborating with outside instructors allowed the library to serve as a hub for a broad range of data instruction and to raise awareness of library services. We plan to offer the series three times in the coming year with an expanding roster of classes.

BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.

OBJECTIVE: The research obtained information to plan data-related products and services. METHODS: Biomedical researchers in an academic medical center were selected using purposive sampling and interviewed using open-ended questions based on a literature review. Interviews were conducted until saturation was achieved. RESULTS: Interview responses informed library planners about researchers' key data issues. CONCLUSIONS: This approach proved valuable for planning data management products and services and raising library visibility among clients in the research data realm.

A need was identified by the Department of Population Health (DPH) for an academic medical center to facilitate research using large, externally funded datasets. Barriers identified included difficulty in accessing and working with the datasets, and a lack of knowledge about institutional licenses. A need to facilitate sharing and reuse of datasets generated by researchers at the institution (internal datasets) was also recognized. The library partnered with a researcher in the DPH to create a catalog of external datasets, which provided detailed metadata and access instructions. The catalog listed researchers at the medical center and the main campus with expertise in using these external datasets in order to facilitate research and cross-campus collaboration. Data description standards were reviewed to create a set of metadata to facilitate access to both externally generated datasets, as well as the internally generated datasets that would constitute the next phase of development of the catalog. Interviews with a range of investigators at the institution identified DPH researchers as most interested in data sharing, therefore targeted outreach to this group was undertaken. Initial outreach resulted in additional external datasets being described, new local experts volunteering, proposals for additional functionality, and interest from researchers in inclusion of their internal datasets in the catalog. Despite limited outreach, the catalog has had ~250 unique page views in the three months since it went live. The establishment of the catalog also led to partnerships with the medical center’s data management core and the main university library. The Data Catalog in its present state serves a direct user need from the Department of Population Health to describe large, externally funded datasets. The library will use this initial strong community of users to expand the catalog and include internally generated research datasets. Future expansion plans will include working with DataCore and the main university library

OBJECTIVE: To evaluate evidence from published randomised controlled trials (RCTs) for the use of task-shifting strategies for cardiovascular disease (CVD) risk reduction in low-income and middle-income countries (LMICs). DESIGN: Systematic review of RCTs that utilised a task-shifting strategy in the management of CVD in LMICs. DATA SOURCES: We searched the following databases for relevant RCTs: PubMed from the 1940s, EMBASE from 1974, Global Health from 1910, Ovid Health Star from 1966, Web of Knowledge from 1900, Scopus from 1823, CINAHL from 1937 and RCTs from ClinicalTrials.gov. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We focused on RCTs published in English, but without publication year. We included RCTs in which the intervention used task shifting (non-physician healthcare workers involved in prescribing of medications, treatment and/or medical testing) and non-physician healthcare providers in the management of CV risk factors and diseases (hypertension, diabetes, hyperlipidaemia, stroke, coronary artery disease or heart failure), as well as RCTs that were conducted in LMICs. We excluded studies that are not RCTs. RESULTS: Of the 2771 articles identified, only three met the predefined criteria. All three trials were conducted in practice-based settings among patients with hypertension (2 studies) and diabetes (1 study), with one study also incorporating home visits. The duration of the studies ranged from 3 to 12 months, and the task-shifting strategies included provision of medication prescriptions by nurses, community health workers and pharmacists and telephone follow-up posthospital discharge. Both hypertension studies reported a significant mean blood pressure reduction (2/1 mm Hg and 30/15 mm Hg), and the diabetes trial reported a reduction in the glycated haemoglobin levels of 1.87%. CONCLUSIONS: There is a dearth of evidence on the implementation of task-shifting strategies to reduce the burden of CVD in LMICs. Effective task-shifting interventions targeted at reducing the global CVD epidemic in LMICs are urgently needed.