scholarly journals Methods for training collaborative biostatisticians

2020 ◽  
pp. 1-13
Author(s):  
Gina-Maria Pomann ◽  
L. Ebony Boulware ◽  
Shari Messinger Cayetano ◽  
Manisha Desai ◽  
Felicity T. Enders ◽  
...  
Keyword(s):  
Translational Research ◽  
Research Collaboration ◽  
Graduate Programs ◽  
Training And Development ◽  
Full Time ◽  
Train The Trainer ◽  
Training Strategy ◽  
Time Position ◽  
Collaboration Process ◽  
Clinical And Translational Research

Abstract The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles. The training strategy also incorporates a “train the trainer” approach to enable CBs who have successfully completed training to train new staff or faculty.

scholarly journals 3369 Assessment to Action: Engaging network member’s in identifying needs and directions of network improvement

2019 ◽  
Vol 3 (s1) ◽  
pp. 130-130
Author(s):  
Paul Estabrooks ◽  
LaKaija Johnson ◽  
Jolene Rohde ◽  
Carol Geary ◽  
Lani Zimmerman ◽  
...  
Keyword(s):  
Translational Research ◽  
Research Collaboration ◽  
Planning Process ◽  
Action Planning ◽  
Data Access ◽  
Database Design ◽  
Research Network ◽  
Recommendations For Action ◽  
Wide Range ◽  
Clinical And Translational Research

OBJECTIVES/SPECIFIC AIMS: To complete a needs assessment and action planning process that engaged clinical and translational research network members in identifying needs through survey feedback, characterizing the needs in small group sessions, and developing recommendations for action at the network’s annual scientific meeting. METHODS/STUDY POPULATION: The project included (1) a survey of 357 members across partner institutions from the Great Plains IDeA CTR Network, (2) 6 - 90 minute brainstorming sessions to characterize needs identified through survey assessment, and (3) 6 - 60 minute sessions to develop recommendations for network improvement based on the characterization activity. Approximately 75 members participated in the characterization and recommendation sessions. RESULTS/ANTICIPATED RESULTS: Seven areas of need from the survey were identified based upon the frequency of identification by network members (support to move research across the translational spectrum, database design and management, data access and sharing, data analysis, recruitment and retention of subjects, support for members who have submitted grants but were repeatedly unsuccessful, mentoring). Members indicated which characterization sessions they were interested in attending and based on the enrollment numbers needs related to unsuccessful grant submitters and mentoring were combined as were needs related to database design and data access-sharing. Sessions resulted in 8 inter-related recommendations for network action that included to (1) develop GP-CTR directory/registry of clinicians, researchers, system partners, that can be used to identify people that want to be involved in research partnerships or mentoring, (2) create a GP CTR Navigators Program to will provide support to network members throughout the collaborative research and grant preparation process, (3) identify and disseminate information about assets (funding, databases/registries) that exist amongst network partners that can be leveraged by member, (4) develop a searchable repository of evidence-based interventions for T3/T4 efforts, (5) review GP CTR supported professional development, and technological resource offerings and identify potential gaps, (6) facilitate opportunities for peer support/networking, (7) provide guidance to GP CTR network institutions looking to adopt policies that will support translational research collaboration, and (8) identify potential barriers to GP CTR network engagement (i.e., infrastructure, communication, marketing). DISCUSSION/SIGNIFICANCE OF IMPACT: This process allowed for a wide range of network members to contribute to actionable recommendations for CTR leadership to move into action and improve the scientific network’s ability to conduct clinical and translational research.

scholarly journals Participatory Needs Assessment and Action Planning for a Clinical and Translational Research Network

2020 ◽  
pp. 1-23
Author(s):  
LaKaija J. Johnson ◽  
Jolene Rohde ◽  
Mary E. Cramer ◽  
Lani Zimmerman ◽  
Carol R. Geary ◽  
...  
Keyword(s):  
Needs Assessment ◽  
Translational Research ◽  
Action Planning ◽  
Research Network ◽  
Clinical And Translational Research

scholarly journals Clinical and Translational Research and Community Engagement: Implications for Researcher Capacity Building

2012 ◽  
Vol 5 (4) ◽  
pp. 329-332 ◽  
Author(s):  
Linda Sprague Martinez ◽  
Beverley Russell ◽  
Carolyn Leung Rubin ◽  
Laurel K. Leslie ◽  
Doug Brugge
Keyword(s):  
Community Engagement ◽  
Translational Research ◽  
Capacity Building ◽  
Clinical And Translational Research

The career paths of social marketing doctoral graduates

2017 ◽  
Vol 7 (1) ◽  
pp. 18-50 ◽  
Author(s):  
V. Dao Truong
Keyword(s):  
Higher Education ◽  
Social Marketing ◽  
Career Paths ◽  
Research Collaboration ◽  
Full Time ◽  
Doctoral Graduates ◽  
Content Type ◽  
For Profit ◽  
Not For Profit ◽  
The Social

Purpose Although the social marketing field has developed relatively quickly, little is known about the careers of students who chose social marketing as their main subject of study. Such research is important not only because it reveals employment trends and mobility but also because it informs policy making with respect to curriculum development as well as raises governmental and societal interest in the social marketing field. This paper aims to analyse the career pathways of doctoral graduates who examined social marketing as the subject of their theses. Doctoral graduates represent a special group in a knowledge economy, who are considered the best qualified for the creation and dissemination of knowledge and innovation. Design/methodology/approach A search strategy identified 209 doctoral-level social marketing theses completed between 1971 and 2015. A survey was then delivered to dissertation authors, which received 117 valid responses. Findings Results indicate that upon graduation, most graduates secured full-time jobs, where about 66 per cent worked in higher education, whereas the others worked in the government, not-for-profit and private sectors. Currently, there is a slight decline in the number of graduates employed in the higher education, government and not-for-profit sectors but an increase in self-employed graduates. A majority of graduates are working in the USA, the UK, Australia and Canada. Overall, levels of international mobility and research collaboration are relatively low. Originality/value This is arguably the first study to examine the career paths of social marketing doctoral graduates.

High-Quality Peer Review of Clinical and Translational Research

2021 ◽  
Vol 78 (15) ◽  
pp. 1564-1568
Author(s):  
Fred M. Kusumoto ◽  
John A. Bittl ◽  
Mark A. Creager ◽  
Harold L. Dauerman ◽  
Anuradha Lala ◽  
...  
Keyword(s):  
Translational Research ◽  
Peer Review ◽  
High Quality ◽  
Clinical And Translational Research

ARGO, Automatic Record Generator for Oncology: a natural language process-based tool to capture pathology features from onco-hematological reports (Preprint)

2021 ◽  
Author(s):  
Gian Maria Zaccaria ◽  
Vito Colella ◽  
Simona Colucci ◽  
Felice Clemente ◽  
Fabio Pavone ◽  
...  
Keyword(s):  
Natural Language ◽  
Translational Research ◽  
Language Processing ◽  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Anatomical Site ◽  
Cell Of Origin ◽  
Molecular Features ◽  
Clinical And Translational Research

BACKGROUND The unstructured nature of medical data from Real-World (RW) patients and the scarce accessibility for researchers to integrated systems restrain the use of RW information for clinical and translational research purposes. Natural Language Processing (NLP) might help in transposing unstructured reports in electronic health records (EHR), thus prompting their standardization and sharing. OBJECTIVE We aimed at designing a tool to capture pathological features directly from hemo-lymphopathology reports and automatically record them into electronic case report forms (eCRFs). METHODS We exploited Optical Character Recognition and NLP techniques to develop a web application, named ARGO (Automatic Record Generator for Oncology), that recognizes unstructured information from diagnostic paper-based reports of diffuse large B-cell lymphomas (DLBCL), follicular lymphomas (FL), and mantle cell lymphomas (MCL). ARGO was programmed to match data with standard diagnostic criteria of the National Institute of Health, automatically assign diagnosis and, via Application Programming Interface, populate specific eCRFs on the REDCap platform, according to the College of American Pathologists templates. A selection of 239 reports (n. 106 DLBCL, n.79 FL, and n. 54 MCL) from the Pathology Unit at the IRCCS - Istituto Tumori “Giovanni Paolo II” of Bari (Italy) was used to assess ARGO performance in terms of accuracy, precision, recall and F1-score. RESULTS By applying our workflow, we successfully converted 233 paper-based reports into corresponding eCRFs incorporating structured information about diagnosis, tissue of origin and anatomical site of the sample, major molecular markers and cell-of-origin subtype. Overall, ARGO showed high performance (nearly 90% of accuracy, precision, recall and F1-score) in capturing identification report number, biopsy date, specimen type, diagnosis, and additional molecular features. CONCLUSIONS We developed and validated an easy-to-use tool that converts RW paper-based diagnostic reports of major lymphoma subtypes into structured eCRFs. ARGO is cheap, feasible, and easily transferable into the daily practice to generate REDCap-based EHR for clinical and translational research purposes.

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