Assistive Technology Options for Individuals with Quadriplegia
Bibliographic record
Abstract
This project was undertaken in collaboration with Sonia Nurkse, MOT, OTR/L and Bridget Tanner, MSOT, OTR/L, two occupational therapists working on the inpatient rehabilitation unit at MultiCare Good Samaritan Hospital in Puyallup, Washington. A systematic review of the literature was conducted to answer the question, “What are the most effective, up-to-date, and user-friendly assistive technology options to support individuals with quadriplegia in functional tasks?” Five databases were searched and through screening and careful review, 19 articles were selected for critical appraisal. Due to the wide variety of devices, some commercially available and other prototypes, we were unable to compare them and determine a superior device. Rather, the assistive technology (AT) devices were organized into three categories: devices that support computer and typing access, devices that support environmental control, and devices that restore function. \nA binder was developed containing AT software and hardware for individuals with limited to no upper extremity use. The AT binder contains devices that are supported by research and those without evidence. An in-service was organized to present the finished product to collaborators and their OT/PT colleagues. Through this process, it has been determined that there is a need for increased outcome research on AT devices for individuals with quadriplegia. This research has also highlighted the unique role that occupational therapy practitioners have in supporting quadriplegic clients’ independence. Due to the rapid rate of technological advances and developments, it is recommended that practitioners actively work to stay current on assistive technology devices and resources.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".