The Kenyan assistive technology ecosystem: a network analysis
Bibliographic record
Abstract
BACKGROUND: Assistive technology is central to the realization of the rights of persons with disabilities. However, there remains limited access to assistive technology throughout much of the world, with particularly poor access in lower- and middle-income countries. Evaluating stakeholder engagement in assistive technology networks has been used as a successful strategy to understand and address gaps in the assistive technology ecosystem. OBJECTIVE: The objective of this research was to provide an overview of the Kenyan Assistive Technology Ecosystem, including available assistive products and related services, and an understanding of the nature and strength of relationships between stakeholders. METHODS: In this study, we employed an online qualitative stakeholder survey (2021) with representatives of organizations involved in assistive technology in Kenya. RESULTS: The assistive technology network in Kenya is distributed, with Government Ministries and Agencies and Organizations of persons with disabilities central to the network. The strength of relationships is concentrated on awareness and communication, with fewer organizations actively collaborating. Innovation training organizations are not yet well integrated into the network. CONCLUSIONS: Improving access to assistive technology in Kenya will benefit from greater collaboration amongst all assistive technology stakeholders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".