COOK technology to support meal preparation following a severe traumatic brain injury: a usability mixed-methods single-case study in a real-world environment
Bibliographic record
Abstract
Introduction Following a traumatic brain injury (TBI), meal preparation may become challenging as it involves multiple cognitive abilities and sub-tasks. To support this population, the Cognitive Orthosis for coOKing (COOK) was developed in partnership with an alternative residential resource for people with severe TBI. However, little is known about the usability of this technology to support people with TBI living in their own homes.Methods A usability study was conducted using a mixed-methods single-case design with a 35-year-old man with severe TBI living alone at home. The number of assistances provided, time taken and the percentage of unnecessary actions during a meal preparation task were documented nine times to explore the usability of COOK. Interviews were also conducted with the participant to document his satisfaction with COOK. Potential benefits were explored via the number of meals prepared per week.Results The usability of COOK was shown to be promising as the technology helped the participant prepare complex meals, while also reducing the number of assistances needed and the percentage of unnecessary actions. However, several technical issues and contextual factors influenced the efficiency and the participant’s satisfaction with COOK. Despite improving his self-confidence, COOK did not help the participant prepare more meals over time.Conclusion This study showed that COOK was easy to use and promising, despite technical and configuration issues. Results suggest the importance of further technological developments to improve COOK’s usability and fit with the needs of people with TBI living in their own homes.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".