A scoping review of vocational rehabilitation services for racialized disabled job seekers and workers
Bibliographic record
Abstract
Purpose Racialized disabled job seekers and workers continue to face disadvantages in the labour market. This gap in labour market outcomes suggests there is a need to investigate the range of employment supports, also known as vocational rehabilitation (VR) services, available to this population. By examining the entire employment journey, we can thoroughly assess how these supports and services impact their employment outcomes. The purpose of this scoping review is to examine and synthesize the existing evidence about VR and identify gaps in practice with racialized disabled job seekers and workers using supports and services to help them gain employment. Design/methodology/approach An extensive literature search was carried out, encompassing the search of four electronic databases and a review of grey literature. From this search, 73 studies and 6 grey literature documents met the inclusion criteria. Findings The results of the analysis are focused on the intersectional impact of racial and (dis)ability identities on VR experiences and outcomes. The analysis suggests that current VR systems are not structured to address the complex needs of racialized disabled job seekers and workers. Service providers and employers should address this gap and design services that can be tailored for individual needs and strengths. Originality/value The study provides a crucial step towards establishing a clearer and more comprehensive understanding of how the intersection of race and disability affects outcomes and experiences within VR services.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".