Enabling local provision of assistive products in rural South India: an organisational survey of needs, barriers, and facilitators
Bibliographic record
Abstract
OBJECTIVE: Access to assistive products (APs) is essential to maximising function, participation, and inclusion of persons with disabilities. Challenges to AP access in low- and middle-income countries include stigma, costs, supply, and rehabilitation capacity gaps. This study aimed to examine AT access in the context of a low-resource setting in rural South India. Objectives were to examine rehabilitation professionals' perceptions of AP needs, barriers and facilitators of AP provision, and AT knowledge. METHODS: A descriptive study design with a 2-part online survey methodology was utilized. This study was conducted in April-September 2020 at a non-governmental organization (NGO) serving children and adults with disabilities in 3 districts of rural South India. Purposive sampling of NGO's multidisciplinary rehabilitation professionals (N=62) was used. The survey was developed based on WHO's Assistive Products List (APL). Barriers and facilitators were classified according to the principles of AT access. Analyses revealed acceptability, affordability, and availability as the top three barrier themes across disciplines, including poor acceptance by clients/families due to stigma, high AP costs, and a long waitlist for government-provided devices. Acceptability, affordability, and accessibility were the top three facilitator themes, including community awareness, availability of AP funding, client/family education, and AT service provision training. IMPACT: Our study identified key enabling strategies for AT access, aimed at reducing reported barriers. Enabling AP provision was determined to be multi-factorial, aimed at users/ families, service providers, organizations, communities, and policymakers. Local stakeholder groups are crucial to understanding challenges and opportunities to AP provision within a low-resource context.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".