Experience of families in accessing government-led support for children with disabilities in Bangladesh
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to understand the experiences of families in accessing government support (i.e., disability allowances and rehabilitation services) for their children with disabilities (CWDs) in Bangladesh. METHOD: We employed a qualitative descriptive method of study and interviewed 27 family members of CWDs. A thematic analysis was applied to analyze data using the following access dimensions to organize themes: availability, accommodation, accessibility, affordability, acceptability, and awareness. RESULTS: Participants shared both positive and negative experiences across the access dimensions in accessing government support. Participants appreciated the government's effort in providing support to CWDs. In particular, disability allowances and coordinated rehabilitation services at one-stop were important for families. Further, positive attitudes, such as respect and support from providers, were reported by many families. However, a majority of participants reported a long wait time to get the disability allowance for CWDs. Participants also reported that a shortage of rehabilitation professionals in the public sector was a major concern. Finally, inaccessible infrastructure (e.g., facilities and transportation) and stigma were barriers for many participants of the study. CONCLUSION: The results suggest that the government's commitment "on paper" is yet to meet the needs of its intended beneficiaries "in practice". There is a need for policy intervention to address barriers faced by families within the context of current access pathways.Implications for rehabilitationShortage of rehabilitation services and limited availability of disability allowances [negatively] affect family member's access to government-led support for their CWDs in Bangladesh.The government has increased services for people with disability significantly but there is a need for it to ensure the availability of all forms of rehabilitation and increase the quota for disability allowances to meet the needs of families.It is imperative to improve mechanisms of monitoring the commitment of enacting tangible results from policies in order to ensure equitable distribution of disability allowance and rehabilitation services.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".