Exploring rehabilitation providers’ perspectives of assistive technology access after the implementation of a paediatric AT provision program in rural South India
Bibliographic record
Abstract
A paediatric Assistive Technology (AT) Provision Program was implemented by a non-governmental rehabilitation facility in rural South India to support rehabilitation providers in providing needed AT access for children with disabilities. Capacity-building measures for providers and other supports based on the AT needs, barriers, and facilitators to AT access were implemented that aligned with the AT global report for low-middle income countries (LMIC). This study explores how the initiatives from the AT Provision Program have influenced the perspectives of rehabilitation providers on AT access. Using a qualitative design eight paediatric rehabilitation providers were purposively sampled for virtual semi-structured interviews. Findings were analysed using thematic analysis. Six overarching themes were identified: (1) Stigma associated with AT use, (2) Organisational response to changing needs, (3) Financial factors related to family socioeconomic status and the organisation providing AT services, (4) Inequity of AT service access in rural areas, (5) Provider AT awareness and confidence and, (6) Quality assurance. Rehabilitation providers' experiences informed future AT capacity-building strategies within a low-resource context. Our findings provide valuable insights for the development of comprehensive AT Provision Program initiatives to provide AT access for children with disabilities in LMIC settings.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".