P3‐027: COOK TO SUPPORT INDEPENDENCE AND SAFETY DURING MEAL PREPARATION: CLINICIANS AND CAREGIVERS PERSPECTIVE
Bibliographic record
Abstract
Promoting the independence and safety of older adults with cognitive impairment during meal preparation is a challenge for occupational therapists. Assistive technology, such as COOK, can be a solution for this problem. Composed of two modules (cognitive assistance and security), COOK offers personalized support on a touch screen installed at the stove to support autonomy while correcting situations at risk. The purpose of this study is to explore the functional profiles of elderly people living with mild cognitive impairment (MCI) or Alzheimer's disease (AD) during meal preparation and to document the relevance of using COOK with this population. 5 focus groups were conducted : 4 with occupational therapists (OTs) working in psychogeriatric clinical setting and 1 with professional caregivers involved with people living with AD (n = 29). The verbatims of these meetings were analyzed according to the qualitative approach of Miles & Huberman. OTs identified different needs and types of intervention according to the diagnosis. COOK reveals great potential according to the interviewed OTs and caregivers, but they also raised some financial and institutional barriers. Experience was brought as an important factor in the integration of an assistive technology such as COOK. The use of COOK to optimize the safety and independence of people living with MCI and/or AD would be achievable. However, it necessary to conduct usability testing to assess the applicability of COOK. 1. Miles, M. B., Huberman, M. A. (2014). Analyse des données qualitatives (4 ed.). Paris: De Broeck.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".