Hiring people with disabilities: A scoping review
Bibliographic record
Abstract
BACKGROUND: Many people with disabilities continue to encounter challenges trying to secure employment. OBJECTIVE: The purpose of this study was to synthesize existent knowledge about the hiring process for people with disabilities and explore research priorities from the perspective of key stakeholders. METHODS: A scoping review of the literature related to hiring processes and practices as they relate to people with disabilities was undertaken. As part of the scoping review, seven key informant consultations were conducted in order to gain further insight into the key issues identified by those most involved in the hiring process for people with disabilities. RESULTS: Findings from the literature and consultations revolve around seven inter-related topics: 1) regulationsversus practice, 2) stigma, 3) disclosure, 4) accommodations, 5) relationship building and use of disability organizations,6) information and support to employers, and 7) hiring practices that invite people with disabilities. CONCLUSIONS: Although barriers to employment for people with disabilities have been examined in the literature, there remains a paucity of literature examining and evaluating strategies to improve hiring practices and employment opportunities for people with disabilities. Future research must occur in consultation with key stakeholders including employers, people with disabilities, and employment support workers.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".