Exploring the psychosocial impact of simple robotic assistive technology on adolescents with neuromuscular disease
Bibliographic record
Abstract
Introduction: Adolescents with neuromuscular disease face significant challenges accessing social leisure activities. Assistive technology has provided new opportunities for those with neuromuscular disease to augment their social lives and leisure pursuits. However, there is limited research evaluating the impact of these technologies. Methods: This study employed mixed methods to evaluate the potential impact of simple robotics on psychosocial factors and quality of life for those adolescents living with neuromuscular diseases. Semi-structured qualitative interviews were performed, as well as the adult Psychosocial Impact of Assistive Technology Device (PIADS) with 9 adolescents, ranging in age from 13 to 19 years old. Results: Thematic analysis of the qualitative data resulted in five major themes: everyday lives and seasonality; socialization; leisure activities; robotics as a leisure activity; and ease of use of robotics. The PIADS found the individual scores demonstrated a range from very little yet positive perceived impact to maximum positive impact in each subscale (competence, adaptability, and self-esteem). Conclusions: Simple mainstream robotics, paired with personalized access methods to control them, offer potential leisure and social integration opportunities to adolescents with neuromuscular diseases in a variety of settings, indoors and outdoors. The findings of this study suggest there are opportunities for this type of mainstream technology to be applied not just to adolescents, but potentially children of all ages with neuromuscular disease, across a variety of environments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".