A Socioeconomic Vulnerability Index for Persons with Disabilities via Machine Learning Approach.

Emre Umucu, Alfonso JRojas Alvarez, Teresa Granger, Aylin Celik Zencir, Alicia Swan, Seeun Park, Beatrice Lee

Abstract read

In one paragraph

Article in Journal of occupational rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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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

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Emre UmucuPublic Health Sciences and Dean's Office, College of Health Sciences Dean's Office, The University of Texas at El Paso, 500 W. University Avenue, El Paso, TX, 79968, USA. eumucu@utep.edu.

Alfonso JRojas AlvarezPublic Health Sciences and Dean's Office, College of Health Sciences Dean's Office, The University of Texas at El Paso, 500 W. University Avenue, El Paso, TX, 79968, USA.

Teresa GrangerDepartment of Educational Studies in Psychology, Research Methodology, and Counseling, University of Alabama, Tuscaloosa, AL, USA.

Aylin Celik ZencirDepartment of Rehabilitation Psychology and Special Education, The University of Wisconsin Madison, Madison, WI, USA.

Alicia SwanAudie L. Murphy Memorial VA Hospital, South Texas VA Medical Center, San Antonio, TX, USA.

Seeun ParkDepartment of Rehabilitation Medicine, University of Washington, Seattle, WA, USA.

Beatrice LeeDepartment of Rehabilitation Sciences, The University of Texas at El Paso, El Paso, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Employment is a key social determinant of health, yet people with disabilities (PWDs) face persistent disparities in vocational rehabilitation (VR) services and outcomes. Federally funded Vocational Rehabilitation (VR) programs serve individuals whose disabilities create barriers to competitive integrated employment, yet service allocation and outcomes vary widely across socioeconomic contexts. Understanding how cumulative socioeconomic disadvantage shapes VR service delivery and employment outcomes is essential for advancing equity. Using Rehabilitation Service Administration (RSA-9/11) data (n = 182,266), machine learning methods, including principal component analysis (PCA), were applied to develop a Socioeconomic Vulnerability Index, which was then used to examine associations with VR service delivery and employment outcomes. Results revealed that higher vulnerability was linked to increased likelihood of receiving basic support services but lower odds of career-oriented services. Higher vulnerability index scores correlated with higher expenditures (+ $246) yet substantially lower wages (- $3.13/hour). Findings suggest that an intake‑level vulnerability screening tool could support risk stratification and more targeted, equity‑focused VR service planning.

Indexed as

EmploymentMachine learningOccupational health policySocial determinants of health social and economic vulnerabilityVocational rehabilitation

Identifiers

PMID42440257

DOI10.1177/00343552251377379

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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.