Personas for the translational workforce

Gonzales, Sara; O'Keefe, Lisa Lea; Gutzman, Karen E; Viger, Guillaume; Wescott, Annie; Farrow, Bailey; Heath, Allison P.; Kim, Meen Chul; Taylor, Deanne; Champieux, Robin; Yen, Po-Yin; Holmes, Kristi

doi:10.1017/cts.2020.2

Cited by 13 publications

(14 citation statements)

References 5 publications

(4 reference statements)

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“…9 Since the system is designed to work with any research output type and can also be integrated with third‐party tools such as GitHub 10 and Jupyter Notebooks, preservation of NMEDW research reports is an ideal use case for this system. This work is part of our orchestrated efforts to enrich the sociotechnical environment at Northwestern through projects to improve communication and training for the clinical and translational workforce, 11 , 12 properly attribute contributors of research outputs, enhance data practice and security workflows, and incorporate standards and persistent identifiers throughout. Some cRDM workshops (notably “Making Your Data FAIR” and “Sharing and Preserving Biomedical Data”) incorporate InvenioRDM to introduce best practices in data sharing.…”

Section: Resultsmentioning

confidence: 99%

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Carson

Gonzales

Shaw

et al. 2022

Learning Health Systems

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Introduction Enterprise data warehouses (EDWs) serve as foundational infrastructure in a modern learning health system, housing clinical and other system‐wide data and making it available for research, strategic, and quality improvement purposes. Building on a longstanding partnership between Northwestern University's Galter Health Sciences Library and the Northwestern Medicine Enterprise Data Warehouse (NMEDW), an end‐to‐end clinical research data management (cRDM) program was created to enhance clinical data workforce capacity and further expand related library‐based services for the campus. Methods The training program covers topics such as clinical database architecture, clinical coding standards, and translation of research questions into queries for proper data extraction. Here we describe this program, including partners and motivations, technical and social components, integration of FAIR principles into clinical data research workflows, and the long‐term implications for this work to serve as a blueprint of best practice workflows for clinical research to support library and EDW partnerships at other institutions. Results This training program has enhanced the partnership between our institution's health sciences library and clinical data warehouse to provide support services for researchers, resulting in more efficient training workflows. Through instruction on best practices for preserving and sharing outputs, researchers are given the tools to improve the reproducibility and reusability of their work, which has positive effects for the researchers as well as for the university. All training resources have been made publicly available so that those who support this critical need at other institutions can build on our efforts. Conclusions Library‐based partnerships to support training and consultation offer an important vehicle for clinical data science capacity building in learning health systems. The cRDM program launched by Galter Library and the NMEDW is an example of this type of partnership and builds on a strong foundation of past collaboration, expanding the scope of clinical data support services and training on campus.

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Section: Resultsmentioning

confidence: 99%

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Carson

Gonzales

Shaw

et al. 2022

Learning Health Systems

Self Cite

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“…Intervention developers do not need to start from scratch in understanding the needs of Translational Teams and the context in which they operate. For example, the Clinical and Translational Science Personas project has described key aspects of those working in CTR 27 . The SciTS literature, too, has described key influences on science team functioning, such as attitudes towards collaboration, leadership, trust, and incentives to collaborate, that can serve as background for the needs assessment.…”

Section: Phase 1: Discovermentioning

confidence: 99%

“…Identify and assess the characteristics, needs, and challenges. Assessing the needs of translational scientists and teams can be achieved using quantitative and qualitative data collected through needs assessments or structured observation [26]. An important component of this phase is convening a group of stakeholders to define and understand the problem space of interest, taking the ecosystem perspective-discovering needs of the priority population (individuals), the teams in which they operate, and the institution/institutional multiteam system that may influence the implementation of the intervention.…”

Section: Activitiesmentioning

confidence: 99%

Enhancing translational team effectiveness: The Wisconsin Interventions in Team Science framework for translating empirically informed strategies into evidence-based interventions

Rolland

Hohl

Johnson

2021

J. Clin. Trans. Sci.

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“…Our methodology in 2019 [5] involved three phases: Identify key CTS roles Identify required elements for outlining Personas (Fig. 1) Develop the Persona profiles based on information retrieved from literature searches and interviews …”

Section: Introductionmentioning

confidence: 99%

Clinical and Translational Science Personas: Expansion and use cases

Gonzales¹,

Champieux²,

Contaxis³

et al. 2023

J. Clin. Trans. Sci.

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Twelve evidence-based profiles of roles across the Clinical and Translational Science (CTS) workforce and two patient profiles were developed by CTS Personas collaborators in 2019 as part of the CTSA Program National Center for Data to Health (CD2H). Based on feedback received from the community, CTS Personas team members collaborated to produce five additional Personas to broaden representation of the CTS workforce and enhance the existing portfolio. This paper presents the rationale and methodology used in the latest CTS Personas initiative. This work also includes an implementation scenario incorporating multiple Personas. Using the new National Institutes of Health’s (NIH) Data Management and Sharing Policy as an example, we demonstrate how administrators, researchers, support staff, and all CTS collaborators can use the Personas to respond to this new policy while considering the needs of service providers and users, CTS employees with short- and long-term needs, and interdisciplinary perspectives.

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Personas for the translational workforce

Cited by 13 publications

References 5 publications

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Bridging the gap: A library‐based collaboration to enhance data skills for clinical researchers

Enhancing translational team effectiveness: The Wisconsin Interventions in Team Science framework for translating empirically informed strategies into evidence-based interventions

Clinical and Translational Science Personas: Expansion and use cases

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