Workforce Transition and Job Accessibility among Ethiopian People with Disabilities: A Cross-Sectional Survey
Bibliographic record
Abstract
Background: In Ethiopia, people with disabilities (PwDs) face significant marginalization and barriers to employment, despite a legislative framework intended to guarantee their rights. A critical knowledge gap exists regarding the implementation of these policies and the lived experiences of highly educated PwD navigating the transition to work. Methods: A cross-sectional survey was conducted in 2021 with 784 university graduates with mobility and/or sensory impairments employed in the public sector. Data on socio-demographics, educational and employment histories, access to workplace accommodations, and perceptions of disability policies were collected via semi-structured questionnaires. Results: Findings reveal a notable gap between policy intent and actual implementation. While participants held generally positive views of disability-related laws, over 60% reported significant frustration during their job search, citing discriminatory vacancy announcements and inaccessible application processes as key barriers. Although higher educational attainment was significantly associated with increased access to workplace accommodations, these provisions remained overwhelmingly insufficient. Critical accommodations like accessible transportation, physical modifications to the work environment, and adaptive equipment were identified as unmet needs by a large majority (68–85%) of respondents. Conclusion: The study concludes that existing laws in Ethiopia have limited translation into equitable employment outcomes for graduates with disabilities. Systemic barriers to PwDs were identified, and these barriers were reported to perpetuate exclusion. Meaningful inclusion demands stricter enforcement of existing laws, deliberate efforts to eliminate attitudinal and environmental barriers, and a firm commitment to guaranteeing workplace accommodations as rights rather than privileges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".