Evidence map›Paper›PMID 40991817›Full record

ArticleRehabilitation psychology2026

The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based participatory research design.

Paul B Perrin, Bryan R Christ, Tiffanie A Vargas, Mia E Dini, Benjamin Ertman, Steph L Cull, Diego Rivera, Bridget Xia, Erin E Andrews-Ash, Linda Mona and 2 more

Abstract read
In one paragraph

Article in Rehabilitation psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

12 authors.

Paul B PerrinSchool of Data Science, University of Virginia.ORCID 0000-0003-2070-215X
Bryan R ChristSchool of Data Science, University of Virginia.
Tiffanie A VargasCentral Virginia Veterans Affairs Health Care System.
Mia E DiniDepartment of Psychology, University of Virginia.
Benjamin ErtmanDepartment of Psychology, University of Virginia.
Steph L CullDepartment of Psychology, Virginia Commonwealth University.
Diego RiveraDepartment of Health Science, Public University of Navarre.
Bridget XiaSchool of Data Science, University of Virginia.
Erin E Andrews-AshVISN 17 Mental Health Clinical Resource Hub, Veterans Affairs Texas Valley Coastal Bend Healthcare System.
Linda MonaVeterans Affairs Long Beach Healthcare System.
Alexander J GatesSchool of Data Science, University of Virginia.
Daniel W KlyceCentral Virginia Veterans Affairs Health Care System.

Funding

US Department of Veterans Affairs; Veterans Health AdministrationVA VA 999999
6 · The paper itself

Abstract

objectiveThis study (a) used OpenAI's GPT-4 large language model to generate an initial item pool for a potential scale measuring internalized ableism, (b) involved disabled community stakeholders in refining the items and prompting additional artificial intelligence-generated items, and (c) psychometrically validated the scale in a large sample of disabled individuals.

methodFollowing a series of GPT-4 prompts and iterative community-based participatory research feedback, a tentative item pool of 90 statements was developed. A sample of 409 adults with diverse disabilities completed a survey containing the initial item pool, potentially related scales, and demographic questions.

resultsAn exploratory factor analysis helped identify the final 51 items and subscale structure, and a confirmatory factor analysis then provided evidence of excellent factor structure fit. The scale contained eight subscales with Cronbach's αs that ranged from .85 to .97, with an overall total score α of .98. The total score and subscales showed consistent convergent validity with other measures of internalized stigma for chronic illness and anger and frustration with disability.

conclusionThis study generated for the first time in the known research literature a nuanced, comprehensive, and psychometrically sound scale based on the integration of both artificial intelligence and community-based participatory research methodology: the Internalized Ableism Inventory. The demonstrated methodology generating it has the potential to modernize psychological scale development approaches. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Indexed as

Artificial IntelligencePersons with DisabilitiesAdultAgedCommunity-Based Participatory ResearchDisability DiscriminationFactor Analysis, StatisticalFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedPsychometricsReproducibility of ResultsSurveys and QuestionnairesYoung Adult

Identifiers

PMID40991817
PMCPMC12462894

What OpenQuestion holds

Textmetadata
LicenceTDM
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.