Co-development of a configuration interface for the COOK cognitive orthosis for meal preparation: a human-centered design study with occupational therapists
Bibliographic record
Abstract
People who sustain a traumatic brain injury can benefit from the use of assistive technologies for cognition, like the Cognitive Orthosis for coOKing (COOK). However, such tools may require personalization for effective use. Although COOK includes multiple personalization options, occupational therapists face challenges with understanding how to configure it due to its complexity and their lack of training. This study thus aimed to: (a) Co-develop a configuration interface that could provide a better User eXperience (UX) for occupational therapists when configuring COOK; and (b) Document the anticipated UX of a mock-up of the configuration interface. First, a mock-up of COOK's configuration interface, named Config My COOK, was co-developed. Second, a qualitative descriptive research design was used to explore the perspective of occupational therapists from Quebec and Ontario using six online focus groups involving 15 participants. Inductive thematic analysis was conducted. Occupational therapists highlighted three features that had the potential to enhance UX, like the appealing look and perceived intuitivity of the configuration interface, support offered by the interface, and access to features to personalize COOK to clients' needs. However, four features had the potential to lessen it, including anticipated training requirements, time required for the configuration process, data confidentiality, and anticipated complexity of interacting with the interface. To counteract these obstacles, occupational therapists identified improvement suggestions which could be completed in the prototype phase. The human-centered design approach enabled us to design a configuration interface to personalize COOK to specific clients' needs, and document its mock-up's anticipated UX with occupational therapists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".