Assistive devices for children with functional impairments: impact on child and caregiver function
Bibliographic record
Abstract
Functional impairments can limit a child's ability to participate in the experiences of childhood. This'deprivation'can, in turn, have a negative effect on such children's development, academic performance, and quality of life, as well as on the lives of their caregivers and families. Many adults use assistive devices to overcome functional impairments and enable them to participate in daily activities; however, such devices may be underutilized by children. Each of the 54 studies reviewed in this report identified one or more functional impairments towards which an assistive device was targeted: accessing a computer (n=3 [studies]), activity assistance (n=2), behaviour changes (n=3), communication (n=30), independent feeding (n=1), living skills (n=1), mobility (n=9), modifying the environment (n=1), nutrition (n=4), and postural stability (n=2). The aim of this review was to determine the impact of assistive devices on the components of functioning defined by the World Health Organization's International Classification of Functioning, Disability and Health. The impact of these devices was found to be overwhelmingly positive. Study outcomes reported were mainly child-focused and could be classified as influencing activity, participation, and personal contextual factors, with relatively little attention paid to caregiver-focused outcomes. Few studies provided either qualitative evidence or experimentally-based quantitative research evidence using controls.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".