Enabling appropriate personnel skill-mix for progressive realization of equitable access to assistive technology
Bibliographic record
Abstract
BACKGROUND AND METHODS: This paper reviews the current capacity of personnel in enabling access to assistive technology (AT) as well as the systems and processes within which they work, and was reviewed, discussed, and refined during and following the Global Research, Innovation, and Education in Assistive Technology (GREAT) Summit. FINDINGS: Key concepts addressed include a person-centred team approach; sustainability indicators to monitor, measure, and respond to needs for service design and delivery; education, research, and training for competent practice, using the six rehab-workforce challenges framework; and credentialing frameworks. We propose development of a competence framework and associated education and training programs, and development and implementation of a certification framework for AT personnel. CONCLUSIONS: There is a resolve to address the challenges faced by People globally to access assistive technology. Context specific needs assessment is required to understand the AT Personnel landscape, to shape and strengthen credentialing frameworks through competencies and certification, acknowledging both general and specific skill mix requirements. Implications for Rehabilitation Personnel in assistive technology (AT) provision should be trained using a person-centred team approach, which emphasizes appropriate skill-mix to address multiple needs within the community. Sustainability indicators should be used which allow personnel to monitor, measure and respond to needs for service design and delivery. A competence framework with associated education and training program, coupled with the development and implementation of a certification framework for AT personnel needs, will promote quality in AT personnel training globally.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".