Ableism and Employment: A Scoping Review of Literature
Bibliographic record
Abstract
Background: Ableism obstructs employment equity for disabled individuals. However, despite protective legislation, research lacks a comprehensive understanding of how ableism multidimensionally manifests across job types, disability types, stages of employment, and intersecting identities. Objectives: This scoping review examined how ableism affects disabled workers and jobseekers, as well as its impacts on employment outcomes, variations across disabilities and identities, and best practices for addressing these. Eligibility Criteria: Included articles were 109 peer-reviewed, empirical studies conducted in the US, Canada, Australia, New Zealand, the UK, Ireland, Denmark, Sweden, Norway, and Finland between 2018 and 2023. Sources of Evidence: Using terms related to disability, ableism, and employment, databases searched included Sociology Collection, CINAHL, PsycInfo, Web of Science, SCOPUS, Education Source, Academic Search Complete, and ERIC. Charting Methods: Data were extracted in tabular form and analyzed through thematic narrative synthesis to identify study characteristics, ableist barriers within employment, intersectional factors, and best practices. Results: Ableism negatively impacted employment outcomes through barriers within the work environment, challenges disclosing disability, insufficient accommodations, and workplace discrimination. Intersectional factors intensified inequities, particularly for BIPOC, women, and those with invisible disabilities. Conclusions: Systemic, intersectional strategies are needed to address ableism, improve policies, and foster inclusive workplace practices.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".