From individual innovation to global impact: the Global Cooperation on Assistive Technology (GATE) innovation snapshot as a method for sharing and scaling
Bibliographic record
Abstract
Assistive technology (AT) is an essential facilitator of independence and participation, both for people living with the effects of disability and/or non-communicable disease, as well as people aging with resultant functional decline. The World Health Organization (WHO) recognizes the substantial gap between the need for and provision of AT and is leading change through the Global Cooperation on Assistive Technology (GATE) initiative. Showcasing innovations gathered from 92 global researchers, innovators, users and educators of AT through the WHO GREAT Summit, this article provides an analysis of ideas and actions on a range of dimensions in order to provide a global overview of AT innovation. The accessible method used to capture and showcase this data is presented and critiqued, concluding that "innovation snapshots" are a rapid and concise strategy to capture and showcase AT innovation and to foster global collaboration. Implications for Rehabilitation Focal tools such as ePosters with uniform data requirements enable the rapid sharing of information. A diversity of innovative practices are occurring globally in the areas of AT Products, Policy, Provision, People and Personnel. The method offered for Innovation Snapshots had substantial uptake and is a feasible means to capture data across a range of stakeholders. Meeting accessibility criteria is an emerging competency in the AT community. Substantial areas of common interest exist across regions and globally in the AT community, demonstrating the effectiveness of information sharing platforms such as GATE and supporting the idea of regional forums and networks.
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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 | 0.034 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".