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Record W4399326590 · doi:10.1080/17483107.2024.2360125

Exploring rehabilitation providers’ perspectives of assistive technology access after the implementation of a paediatric AT provision program in rural South India

2024· article· en· W4399326590 on OpenAlexaff
Alakshiya Arumuganathan, Iqra Shah, Franzina Coutinho, Dinesh Krishna, Navamani Venkatachalapathy, Marie Brien, Andrea Duncan

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRehabilitationAssistive technologyBusinessCapacity buildingLow and middle income countriesNursingEconomic growthMedicineDeveloping countryPhysical therapyComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.006
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.420
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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