The experiences of children with intellectual and developmental disabilities in inclusive schools in Accra, Ghana
Bibliographic record
Abstract
BACKGROUND: Inclusive education is internationally recognised as the best strategy for providing equitable quality education to all children. However, because of the unique challenges they often present, children with intellectual and developmental disabilities (IDDs) are often excluded from inclusive schools. To date, limited research on inclusion has been conducted involving children with IDD as active participants. OBJECTIVES: The study sought to understand the experiences of children with IDDs in learning in inclusive schools in Accra, Ghana. METHOD: A qualitative descriptive design was utilised with 16 children with IDDs enrolled in inclusive schools in Accra, Ghana. Participants were recruited through purposive sampling and data were collected using classroom observations, the draw-and-write technique and semi-structured interviews. The data were analysed to identify themes as they emerged. RESULTS: Children's experiences in inclusive schools were identified along three major themes: (1) individual characteristics, (2) immediate environments and (3) interactional patterns. Insights from children's experiences reveal that they faced challenges including corporal punishment for slow performance, victimisation and low family support relating to their learning. CONCLUSION: Although children with IDDs receive peer support in inclusion, they experience diverse challenges including peer victimisation, corporal punishment and low family and teacher support in their learning. Improvement in inclusive best practices for children with IDD requires systematic efforts by diverse stakeholders to address identified challenges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".