Evidence map›Paper›PMID 40823685›Full record

ArticleJournal of statistics and data science education : an official journal of the of the American Statistical Association2025

Diagnosing Statistical Education Needs of Health Science Learners.

Amy S Nowacki, Ann M Brearley, Robert A Oster, Emily Slade, Katrina L Devick, Matthew J Hayat, Sally W Thurston

Abstract read
In one paragraph

Article in Journal of statistics and data science education : an official journal of the of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Amy S NowackiDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195.
Ann M BrearleyDivision of Biostatistics and Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN 55455.
Robert A OsterDepartment of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL 35294.
Emily SladeDepartment of Biostatistics, University of Kentucky, Lexington, KY 40508.
Katrina L DevickDepartment of Quantitative Health Sciences, Mayo Clinic, Scottsdale, AZ 85259.
Matthew J HayatDepartment of Population Health Sciences, School of Public Health, Georgia State University Atlanta, GA 30303.
Sally W ThurstonDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester NY 14642.

Funding

National Dissemination of I-Corps@NCATS: Accelerating Translation through CommercializationUL1TR003096 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI GUTIERREZ, ORLANDO M, KIMBERLY, ROBERT P. · 2019 to 2023
$43.6M
Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022
$34.9M
The University of Rochester's Clinical and Translational Science InstituteUL1TR002001 · NCATS · UNIVERSITY OF ROCHESTER · PI WILSON, KAREN M., ZAND, MARTIN S · 2016 to 2024
$34.6M
Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
Clinical and Translational Science Collaborative of Northern Ohio, Catalyzing Linkages to Equity in Health (CLE Health)UM1TR004528 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI GRACE A MCCOMSEY · 2023 to 2026
$32.1M
NCATS NIH HHS UL1 TR001998NCATS NIH HHS UL1 TR002001NCATS NIH HHS UL1 TR002494NCATS NIH HHS UL1 TR002548NCATS NIH HHS UL1 TR003096NCATS NIH HHS UM1 TR004528
6 · The paper itself

Abstract

Many types of health science learners, including clinical and translational scientists, students, researchers, and clinicians, seek to increase their knowledge of biostatistics. These learners are heterogeneous in their field, career stage and career focus. Based on the collective experience of an expert panel with over 115 years teaching statistics to health science learners, we propose a framework for considering the needs of health science learners motivated by their career goals. This framework defines four types of health science learners seeking statistical training: (1) consumers, (2) "milestone makers", (3) biomedical researchers with statistical support, and (4) biomedical researchers without statistical support. Each type of learner has different levels at which they need to understand statistical topics for their careers, such as when to use a particular statistical method or why a given method works; these differing levels of understanding are detailed in our proposed framework. Further, this framework identifies the expectations that each of these types of learners should have for gaining statistical knowledge in a single seminar, multiple seminars, a seminar series, an accredited course, or a certificate/degree program. Advantages and disadvantages of widely used educational formats for these learners are also described. From this work, health science learners seeking biostatistical training or those who are planning a training program for others can gain insight into identifying appropriate statistical training goals for the type of learner with which they identify. Statistical educators may also use these guidelines to help health science learners align expectations for various types of training.

Indexed as

biostatisticshealth science learnerresearch trainingstatistical competency

Identifiers

PMID40823685
PMCPMC12352506

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.