Evidence map›Paper›PMID 41822555›Full record

ArticleJournal of clinical and translational science2026

SPIE: An organizational framework to facilitate accessibility in academic statistical consulting centers.

Marianne Huebner, Michael Wallace, Felesia Stukes, Sam Manski, Maria Montez-Rath

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2026. 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

5 authors.

Marianne HuebnerStatistics and Probability, Michigan State University, USA.ORCID https://orcid.org/0000-0002-9694-9231
Michael WallaceDepartment of Statistics and Actuarial Science, University of Waterloo, Canada.
Felesia StukesDepartment of Computer Science, Engineering, and Mathematics, Johnson C Smith University, USA.
Sam ManskiData Innovation Lab, Tech Impact, USA.
Maria Montez-RathQuantitative Sciences Unit, Stanford University School of Medicine, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Academic statistical consulting centers collaborate and provide statistical support to faculty and graduate students for their research projects. Clients seeking statistical support may face communication and cognitive accessibility barriers that consultants may not be aware of. Many researchers do not disclose their personal circumstances, such as neurodivergent behaviors. Universal Design for Learning is a framework that aims to support individuals regardless of their abilities or learning styles, by providing multiple means of engagement, presentation, and action and expression. Adapting these principles for statistical consulting and taking time to assess specific needs of individuals helps improve the experiences of clients and consultants and leads to high quality statistical support and successful completion of research projects. We propose an organizational framework applicable to statistical consulting environments aligned with accessibility guidelines to improve access and empower clients and consultants. Providing a respectful environment where differences are normalized and welcomed creates a sense of belonging for everyone.

Indexed as

accessibilityaccommodationneurodivergenceStatistical consultingUniversal Design for Learning

Identifiers

PMID41822555
PMCPMC12975617

What OpenQuestion holds

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LicenceCC BY
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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.