Using a cognitive orthosis to support older adults during meal preparation: Clinicians’ perspective on COOK technology
Bibliographic record
Abstract
INTRODUCTION: Occupational therapists promote safety and autonomy of older adults with cognitive impairments. A technology, named COOK, offers support on a touch screen installed next to the stove to support task performance while correcting risky behaviors. We aimed to document (1) the functional profiles according the diagnosis (2) the types of interventions used to increase autonomy in the kitchen (3) the facilitators and obstacles to the implementation of COOK with this clientele. METHODS: = 24) and were transcribed and analyzed using thematic analysis, including coding and matrix building. RESULTS: Occupational therapists identified different (1) functional profiles and (2) interventions for both diagnoses. The use of COOK (3) could be more beneficial in mild cognitive impairment, as many barriers occur for the use in Alzheimer's disease. Some parameters, such as digital control of the stove and complex information management, need to be simplified. DISCUSSION: According to occupational therapists, this technology is particularly applicable to people with mild cognitive impairment, because this population has better learning abilities. CONCLUSION: This study documented the specific needs of older adults with cognitive impairments as well as interventions used by occupational therapists. The perspectives of caregivers should be captured in future research.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".