Evidence map›Paper›PMID 40556110›Full record

ArticleSociology of health & illness2025

Constructing the Autistic-Transgender Intersection: A Critical Discourse Analysis.

Maddie Kerr, Rebecca Ewert

Abstract read
In one paragraph

Article in Sociology of health & illness, 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

2 authors.

Maddie KerrDepartment of Sociology, Northwestern University, Evanston, Illinois, USA.ORCID 0000-0002-8910-9154
Rebecca EwertDepartment of Sociology, Northwestern University, Evanston, Illinois, USA.ORCID 0000-0003-1333-6382

Funding

Northwestern University Office of Undergraduate Research #OUR-OUR-ACADEMIC-YEAR-19896
6 · The paper itself

Abstract

Researchers increasingly report a statistical intersection between autism and transgender (trans) identities. Claims about this estimated relationship have recently proliferated within and beyond scientific research, particularly amidst escalating political struggles over the rights and recognition of trans people. Our study critically analyses discursive patterns among these claims to investigate how and to what effects diverse actors construct the intersection of autism and trans identities. We qualitatively analysed a purposive sample of 30 texts, including psychological research, clinical literature, news and opinion articles, state legislation, activist materials and autistic trans individuals' published narratives. We propose that claims about the autistic-trans intersection can be understood through four discursive types: psychiatrisation, repressive protection, medical management and affirmation and reclamation. These types represent differently patterned ways of (1) explaining medical vs. social 'causes' of the intersection and (2) legitimising versus delegitimising autistic trans identities, with each type promoting distinctive medical, cultural and political practices as responses. In this case study, we present the autistic-trans intersection as a site at which social actors with varied political aims and differential access to power reproduce and resist normative systems of gender and ability, pursue varied processes of de/medicalisation and negotiate expert knowledge and uncertainty.

Indexed as

Autistic DisorderTransgender PersonsFemaleGender IdentityHumansMalePoliticsQualitative Researchautismdiagnosisdiscourse analysismedicalisationtransgender

Identifiers

PMID40556110
PMCPMC12188031

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.