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Record W4413655736 · doi:10.3390/healthcare13172119

Intersecting Identities and Career-Related Factors Among College Students with Disabilities Across Ethnic Groups

2025· article· en· W4413655736 on OpenAlexaff
Si-Yi Chao, Keith B. Wilson

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsEthnic groupPsychologyMedical educationSociologyMedicineAnthropology

Abstract

fetched live from OpenAlex

This study explores how intersecting disabilities and ethnic identities influence key career-related factors, including career decision self-efficacy, career outcome expectations, perceived career barriers, and social support, among college students with disabilities from diverse racial and ethnic backgrounds. Background/Objectives: Applying social cognitive career theory (SCCT) and intersectionality frameworks, this research addresses a critical gap in understanding the unique challenges and strengths experienced by underrepresented students with disabilities in postsecondary education. Method: Quantitative data were collected from approximately 306 participants representing various ethnic groups, including African American, Asian American, Hispanic, and other ethnic backgrounds, alongside European American peers. Results: Findings revealed that underrepresented students with disabilities reported significantly stronger ethnic identity affirmation but also perceived greater career-related barriers compared to their European American counterparts. These results demonstrate the need for culturally responsive career development practices and inclusive campus environments that affirm students’ multiple identities. Conclusions: Implications are discussed for higher education professionals, rehabilitation counselors, disability service providers, and career counselors seeking to promote equitable career outcomes and identity-conscious support systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.426
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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