The COVID-19 pandemic and the Black Lives Matter movement revealed American society's unconscious bias and systemic discrimination against Black/African Americans and other minority groups, including people with disabilities. These disparities are particularly pronounced for individuals with intersecting identities, such as race, gender, disability, and poverty, leading to racism and ableism.
Objective
This case-control study investigated racial disparities in acceptance rates for state vocational rehabilitation (VR) services among applicants with schizophrenia.
Method
Utilizing the U.S. Department of Education Rehabilitation Services Administration (RSA-911) dataset for the program year 2018, we compared acceptance rates between Black/African American and White American VR applicants with schizophrenia. Propensity score matching (PSM) was performed to create a White applicant with schizophrenia (control, n = 2,971) group and a black applicant with schizophrenia (treatment, n = 2,971) group with similar demographic characteristics to control selection bias.
Results
Our findings indicated no significant difference in VR acceptance rates between the two groups: 73.7% for Black/African Americans with schizophrenia and 72.4% for White Americans with schizophrenia.
Conclusions
This outcome contrasts with previous studies that suggest racial bias still exist in state VR services. Our findings may indicate that, when selection bias is controlled, acceptance rates for VR services between White Americans and Black/African Americans with disabilities may be comparable. This study contributes to the ongoing dialogue on racial equity in disability services, and it presents an alternative research method for conducting racial difference studies in VR.
AtkinsB. J.WrightG. N.HumphreysR. R.ProvittE.BoltonB.CooperP. G. (1980). Three views: Vocational rehabilitation of blacks. The statement, the response, the comment. Journal of Rehabilitation, 46(2), 40–49.
3.
AustinP. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. https://doi.org/10.1080/00273171.2011.568786
4.
BlalockK.ChanF.CardosoE.LeeE. J.IwanagaK.WuJ. R.ChenX. (2024). Psychometric validation of the Scale for Ethnocultural Empathy in a sample of undergraduate and graduate students in rehabilitation: A brief report. Rehabilitation Counseling Bulletin, 67(2), 131–138. httpsss://doi.org/http://doi.org/10.1177/00343552221102395 https://doi.org/10.1177/00343552221102395
5.
BoltonB. F.BelliniJ. L.BrookingsJ. B. (2000). Predicting client employment outcomes from personal history, functional limitations, and rehabilitation services. Rehabilitation Counseling Bulletin, 44(1), 10–21. https://doi.org/10.1177/003435520004400103
6.
BondG. R.Al-AbdulmunemM.MarbacherJ.ChristensenT. N.SveinsdottirV.DrakeR. E. (2023). A systematic review and meta-analysis of IPS supported employment for young adults with mental health conditions. Administration and Policy in Mental Health, 50(1), 160–172. https://doi.org/10.1007/s10488-022-01228-9
7.
CampbellK.BondG. R.DrakeR. E. (2011). Who benefits from supported employment: A meta-analytic study. Schizophrenia Bulletin, 37(2), 370–380. https://doi.org/10.1093/schbul/sbp066
CatalanoD.PerieraA.WuM. Y.ChanF.HoH. (2006). Vocational rehabilitation services as predictors of employment outcomes of clients with TBI. NeuroRehabilitation, 21(4), 279–293.
ChanF.CheingG.ChanJ. Y. C.RosenthalD. A.ChronisterJ. (2006). Predicting employment outcomes of rehabilitation clients with orthopedic disabilities: A CHAID analysis. Disability and Rehabilitation, 28(5), 257–270. https://doi.org/10.1080/09638280500158307
12.
ChanF.IwanagaK.TanseyT. N.DitchmanN.WehmanP.WuJ.ChanX. (2024). Assessing evidence-based disability inclusion policies and employment of people with disabilities in the workplace: Scale development and validation. Rehabilitation Counseling Bulletin. https://doi.org/10.1177/00343552441268757
13.
ChanF.WongD. W.RosenthalD.KunduM.DuttaA. (2005). Eligibility rates of traditional underserved individuals with disabilities: A data mining approach. Journal of Applied Rehabilitation Counseling, 36(3), 3–10. https://doi.org/10.1891/0047-2220.36.3.3
DuttaA.GerveyR.ChanF.ChouC. C.DitchmanN. (2008). Vocational rehabilitation services and employment outcomes for people with disabilities: A United States study. Journal of Occupational Rehabilitation, 18(4), 326–334. https://doi.org/10.1007/s10926-008-9154-z
16.
DziekanK. I.OkochaA. A. (1993). Accessibility of rehabilitation services: Comparison by racial-ethnic status. Rehabilitation Counseling Bulletin, 36(4), 183–189.
17.
FanX.NowellD. L. (2011). Using Propensity Score Matching in educational research. Gifted Child Quarterly, 55(1), 74–79. https://doi.org/10.1177/0016986210390635
18.
Feist-PriceS. (1995). African Americans with disabilities and equity in vocational rehabilitation services: One state's review. Rehabilitation Counseling Bulletin, 39(2), 119–129.
LiauM. Y. Q.TohE. Q.MuhamedS.SelvakumarS. V.ShelatV. G. (2024). Can propensity score matching replace randomized controlled trials?World Journal of Methodology, 14(1), 90590. https://doi.org/10.5662/wjm.v14.i1.90590
25.
LuellenJ. K.ShadishW. R.ClarkM. H. (2005). Propensity scores: An introduction and experimental test. Evaluation Review, 29(6), 530–558. https://doi.org/10.1177/0193841X05275596
26.
O’NeillJ.MamunA. A.PotamitesE.ChanF.CardosoE. (2015). Return to work of disability insurance beneficiaries who do and do not access state vocational rehabilitation agency services. Journal of Disability Policy Studies, 26(2), 111–123. https://doi.org/10.1177/1044207315583900
27.
PruettS. R.ChanF. (2006). The development and psychometric validation of the disability attitude implicit association test. Rehabilitation Psychology, 51(3), 202–213. https://doi.org/10.1037/0090-5550.51.3.202
Rehabilitation Act Amendments of 1992. (1992). Pub. L. No.102-569, 106.
30.
Rehabilitation Services Administration [RSA]. (2016). RSA-911 Case Service Report Manual.
31.
RosenbaumP.R.RubinD.B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
32.
RosenthalD. A.BervenN. L. (1999). Effects of client race on clinical judgment. Rehabilitation Counseling Bulletin, 42(3), 243–264.
33.
RosenthalD. A.FerrinJ. M.WilsonK.FrainM. (2005). Acceptance rates of African-American versus white consumers of vocational rehabilitation services: A meta-analysis. Journal of Rehabilitation, 71(3), 36–44.
