Barriers and Facilitators in Accessing Education in Mainstream Schools for Children with Disabilities in Bangladesh: A Qualitative Secondary Data Analysis
Bibliographic record
Abstract
Promoting access to mainstream schools for children with disabilities has been highlighted in national and international legislative and policy initiatives. However, children with disabilities’ access to mainstream school remains an important challenge, particularly in low and middle-income countries, such as Bangladesh. We explored barriers and facilitators in accessing education for children with disabilities through a secondary analysis of qualitative data. Results indicate that children with disabilities experience challenges in their endeavour to access education, including stigma, discrimination, inaccessible built environment, and teachers’ negative attitudes towards children with disabilities. However, support from different sources, specifically parents’ strong motivation towards their child’s education and peer support, are great facilitators for children with disabilities in accessing education. We conclude with recommendations for support for parents to advocate for the rights of their children with disabilities in mainstream schools. We also recommend that government should invest resources to make mainstream schools disability friendly. In particular, accessible classrooms and washrooms are essential to enable in-class participation of children with disabilities. Further, investment is needed for teachers’ training on teaching disabled children in mainstream schools. A collaborative approach between policymakers, teachers, professionals, and parents is imperative to achieve inclusive education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".