Assistive Products and Technology to Facilitate Activities and Participation for Children with Disabilities
Bibliographic record
Abstract
We aimed to identify activity limitations and participation restrictions encountered by children and youth with disabilities for which assistive products and technology could be helpful. We used a convergent, parallel, mixed-methods design involving a nationwide, French survey composed of closed questions (quantitative) and open questions (qualitative) that enlightened the quantitative data. A total of 1055 responses were received, and 962 included: 92 from children and youth with disabilities, 493 from relatives and 377 from professionals. Difficulties frequently checked and described in detail were participation in recreational activities, leaving the house and traveling, participating in a group, and getting ready. Transversal explanations for difficulties were spontaneously provided (e.g., lack of accessibility and mobility). Solutions proposed included personal assistive devices to facilitate home life, high-tech devices, devices to compensate for impaired body functions, and adaptation of the familiar environment and daily activities. Few public solutions were proposed. The necessity of human assistance was emphasized. The mixed-methods design and involvement of different stakeholders identified common, macroscopic trends in difficulties encountered and desired solutions. Products and technology are required in the following domains: the familiar environment, accessibility and mobility, sports and leisure, high-technology, and family support. We provide suggestions to facilitate the development of innovative solutions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".