Disability stigma and the pursuit of inclusion among children with physical disabilities in northwest Ethiopia
Bibliographic record
Abstract
PURPOSE: Globally, many children with physical disabilities (CwPDs) are excluded from their education and social life, mainly due to stigma and its effects. However, information on disability stigma is scarce in low-and middle-income countries like Ethiopia. The purpose of this study, therefore, was to explore the lived experience of stigma from the perspective of CwPDs and their caregivers in northwest Ethiopia. METHODS: The study incorporated a constructivist grounded theory approach. Data were gathered through observations, in-depth interviews, and key informant interviews. Inductive and iterative grounded theory techniques were used to code and categorize data through constant comparison. FINDINGS: The study showed that CwPDs are bullied and talked about, subject to pity, socially excluded, and held in contempt at home, in their neighborhood, and at school. They live in fear and uncertainty and struggle with stigma. These factors affect their emotions, social relationships, and educational activities. CONCLUSIONS: This study makes theoretical and practical contributions to understanding and addressing disability stigma and can help to introduce inclusive education policy, school practice, and disability advocacy. The study results highlight the need for disability awareness-raising programs, interventions for empowerment, and informing disability policy and practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".