Injury risk compensation in children with disabilities: could assistive technology devices have a dark side?
Bibliographic record
Abstract
PURPOSE: This review article investigates the role of assistive technology (AT) devices and other contextual aspects as unintentional injury risk factors in children with disabilities. METHOD: A literature review was conducted to identify and review empirical studies that examined the role of AT devices, protective equipment (PE), and other consumer products in the risk-taking behaviors of children and their parents. RESULTS: Nine original empirical studies and one systematic review examining changes in the risk-taking behaviors or injury levels associated with children's PE and other products were identified and critically reviewed. None of the articles specifically addressed the compensatory effect of AT devices. Since evidence of changes in the risk tolerance of children and their parents after the introduction of PE and other products for children exists, it is conceivable under certain conditions that AT devices could interact with other known risk factors to promote injury risk-taking behaviors in children and their parents. CONCLUSIONS: Outcomes of this review and current thinking about the interaction of health conditions and contextual factors provide a theoretical underpinning to explore the causal association among unintentional injury risk factors and AT device use by children with disabilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".