Homes of Stroke Survivors Are a Challenging Environment for Rehabilitation Technologies
Bibliographic record
Abstract
The design of digital technologies that support poststroke rehabilitation at home has been a topic of research for some time. If technology is to have a large-scale impact on rehabilitation practice, then we need to understand how to create technologies that are appropriate for the domestic environment and for the needs and motivations of those living there. This paper reflects on the research conducted in the Motivating Mobility project (UK Engineering and Physical Science Research Council: EP/F00382X/1). We conducted sensitizing studies to develop a foundational understanding of the homes of stroke survivors, participatory design sessions situated in the home, and experimental deployments of prototype rehabilitation technologies. We identified four challenges specific to the homes of stroke survivors and relevant to the deployment of rehabilitation technologies: identifying a location for rehabilitation technology, negotiating social relationships present in the home, avoiding additional stress in households at risk of existential stress, and providing for patient safety. We conclude that skilled workers may be needed to enable successful technology deployment, systematizing the mapping of the home may be beneficial, and education is a viable focus for rehabilitation technologies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".