Barriers and Facilitators for Wheelchair Users in Bangladesh: A Participatory Action Research Project
Bibliographic record
Abstract
Purpose: People who use wheelchairs face a range of physical, social, and economic barriers to regular participation in their communities. These barriers may be more acute in countries such as Bangladesh which are affected by poverty and often lack the physical infrastructure or resources necessary to create inclusive or accessible environments. This research aimed to identify: (a) the barriers and facilitators to accessibility faced by wheelchair users in Bangladesh; (b) how these barriers affect the inclusion of wheelchair users in Bangladeshi society; and (c) what could be done to improve accessibility and inclusion for wheelchair users in Bangladesh. Methods: This participatory action research (PAR) project used Photovoice and semi-structured interviews to identify barriers and facilitators to accessibility for people who use wheelchairs in Bangladesh. Results: Participants mentioned a number of barriers in public spaces, such as roads, missing or inadequate ramps, inaccessible restrooms, and negative attitudes. There were also participants who had made their home environments more accessible with accommodations such as ramps, arrangement of space, and low countertops/work spaces. Women wheelchair users seemed to face greater barriers to access, as compared to men, in a range of community spaces and activities. Participants’ recommendations for improvement targeted government stakeholders and included greater focus on road infrastructure, particularly during flooding in the rainy season, and modifications to the public transportation system. Conclusion: A key goal of the study was to identify barriers and facilitators, and use the information gathered to promote social change on the ground. Future research and action should encourage more people to get involved in removing barriers for people with disabilities, in Bangladesh as well as globally.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".