Can we configure COOK, a cognitive Orthosis for meal preparation, with efficiency and effectiveness?
Bibliographic record
Abstract
PURPOSE: Assistive Technologies for Cognition (ATCs), such as the Cognitive Orthosis for coOKing (COOK), offer support to individuals with traumatic brain injury by enhancing safety and independence. While the usability of COOK's client interface has been tested, the expert interface-used by occupational therapists to customize the interface to clients' needs-requires further study. We therefore aimed to: (1) Prioritize the modifications to be made to COOK's configuration interface; and (2) Describe the effectiveness and efficiency of the interface within a laboratory context. MATERIALS AND METHODS: A Human-Centered Design (HCD) approach was used. Fourteen occupational therapists and master's students in occupational therapy participated. A convergent mixed-methods design was used. Data was collected through laboratory testing of the interface. Qualitative data was analyzed using deductive thematic analysis [1], and quantitative data with descriptive statistics. RESULTS: Our study highlighted the importance of balancing effectiveness and efficiency during technology design. Participants emphasized that the configuration process should ensure both a quality that allows customization to meet client needs and an ease of use to minimize the time required for the configuration of COOK. Overall, a good usability of COOK's configuration interface was demonstrated both qualitatively and quantitatively. CONCLUSION: Future studies will be needed to implement the technology in clinical practice and gather further insights on this interface for the design of further iterative improvements to meet the needs of occupational therapists and their clients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".