Enabling appropriate personnel skill-mix for progressive realization of equitable access to assistive technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it