Job satisfaction among people with disabilities in Ethiopia: A cross-sectional survey
Bibliographic record
Abstract
Background: Job satisfaction among people with disabilities (PWDs) is a significant concern because of its impact on productivity, job retention and well-being in the workplace. Objectives: This study aimed to assess the job satisfaction of employees with disabilities in Ethiopia and to identify key factors influencing job satisfaction. Method: A cross-sectional survey was conducted with 784 Ethiopian government employees with various disabilities. All interviews were conducted in 2021.The survey was designed to collect key socio-demographic information, and factors related to job satisfaction. Results: The majority of respondents had motor difficulties (59%), followed by visual impairments (36.7%). The mean age was 33 years, and 67% were male. Over half of the study participants were first-degree holders, and 80.6% had experienced integrated education. The mean time to secure a job was 15.41 months, with over 18% unemployed for 6-12 months. Job dissatisfaction was influenced by factors such as low salary, gender, service years and lack of personal assistance. Vision impairment correlated with higher dissatisfaction. Overall, around 32.5% reported satisfaction in their job, 44.1% were neutral and 23.4% were dissatisfied. Dissatisfaction rose to 29% when measured using supplementary questions. Conclusion: The study was the first to examine factors leading to job satisfaction of employees with disabilities in the Ethiopian public sector. Recommendations include social policy adjustments for better working conditions, considering central factors associated with dissatisfaction. The government should explore measures such as employment quotas or wage supplementation to address disparities and ensure reasonable accommodation. Inclusive research methods will assist in leading change. Contribution: This research contributes nuanced insights into the factors affecting job satisfaction and its complexities among employees with disabilities in the Ethiopian context, emphasising the need for ongoing research to improve worker support structures and inclusive practices in job acquisition and employment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".