Evidence map›Paper›PMID 40495157›Full record

ArticleInternational journal for equity in health2025

Still we rise: research on bias and discrimination will endure.

Gregg S Gonsalves

Abstract readLetterComment
In one paragraph

Article in International journal for equity in health, 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

5 · Who and what money

Authors and funding

1 author.

Gregg S GonsalvesDepartment of Epidemiology of Microbial Diseases, Public Health Modeling Unit, Yale School of Public Health, 350 George Street, Ste 3rd Floor, New Haven, CT, 06511, USA. gregg.gonsalves@yale.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This is a commentary on Reisner et al's Analyzing multiple types of discrimination using implicit and explicit measures, comparing target vs. Dominant groups, in a study of smoking/vaping among community health center members in Boston, Massachusetts (2020-2022). This manuscript is a study of the intersection of multiple forms of discrimination-racism, sexism, heterosexism, cissexism, ageism, and sizeism-and measures of implicit and explicit bias in the context of current smoking and vaping behavior among patients from targeted versus dominant groups at community health centers in Boston, Massachusetts (USA) from 2020 to 2022. The authors used logistic regression to assess smoking and vaping behavior with each type of discrimination, and then extended this analysis employing a meta-regression approach to better understand relationships across all types of discrimination under consideration in their study. Recently, the grant from the US National Institutes of Health, which supported this research was terminated in progress for ideological reasons by the current US administration under President Donald J. Trump for simply focusing on discrimination. While this study was among the first to be terminated by the Trump administration, hundreds of grants from the NIH and other US research funders have been cancelled in the first half of 2025. Reisner et al's paper is an important piece of research, but it represents the start of a sophisticated inquiry into discrimination and bias, and future work by this team and in this area of research is necessary and sadly, now impossible to do with federal scientific funding. Work on discrimination and bias has always faced obstacles, but the scope and scale of attacks on science in the US require all scientists to push back against this censorship and political interference in the funding and conduct of research.

Indexed as

PrejudiceSocial DiscriminationVapingBiasBostonHumansRacismSexismSmokingUnited States

Identifiers

PMID40495157
PMCPMC12153171

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.