Examining Macro and Meso Level Barriers to Hiring Persons with Disabilities: A Scoping Review
Bibliographic record
Abstract
Abstract Purpose The objective of this paper was to identify and analyze barriers to hiring persons with disabilities from the perspective of employers and persons with disabilities. Methodology A scoping review was used to evaluate both evidence and grey literature. An integrative analysis was employed to explicate the most salient macro and meso level barriers that limit the hiring of persons with disabilities. Findings A total of 38 articles from 6,480 evidence literature and 19 documents from grey literature were included in data extraction. Barriers included: negative attitudes in society, by employers and coworkers (macro and meso); workplace barriers (meso) were about lack of employer knowledge of performance skill and capacity of persons with disabilities, and the lack of awareness of disability and the management of disability-related issues in hiring and retention; and service delivery system barriers (macro) were focused on the lack of integration of services and policies to promote hiring and retention. Social implications Knowledge gained furthers the understanding of the breadth of social, workplace and service delivery system obstacles that restrict the entry into the labor marker for persons with disabilities. Originality/value Barriers to employment for persons with disabilities at the macro and meso level are evident in the literature and they remain persistent over time despite best efforts to promote inclusion. Findings in this review point to the need for more specific critical research on the persistence of social, workplace and service delivery system barriers as well as the need for pragmatic approaches to change through partnering and development of targeted information to support employers in hiring and employing persons with disabilities.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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 teacher head, 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".