Implementation of an assistive technology for meal preparation within a supported residence for adults with acquired brain injury: a mixed-methods single case study
Bibliographic record
Abstract
Objectives This study aimed to investigate the feasibility of implementing an assistive technology for meal preparation called COOK within a supported community residence for a person with an acquired brain injury.Methods Using a mixed-methods approach, a multiple baseline single-case experimental design and a descriptive qualitative study were conducted. The participant was a 47-year-old woman with cognitive impairments following a severe stroke. She received 21 sessions of training on using COOK within a shared kitchen space. During meal preparation, independence and safety were evaluated using three target behaviours: required assistance, task performance errors, and appropriate responses to safety issues, which were compared with an untrained control task, making a budget. Benefits, barriers, and facilitators were assessed via three individual interviews with the client and three focus groups with the care team.Results Both quantitative and qualitative analyses showed that COOK significantly increased independence and safety during meal preparation but not in the control task. Stakeholders suggested that the availability of a training toolkit to a greater number of therapists at the residence and installation of COOK within the client’s apartment would help with successful adoption of this technology.Conclusion COOK is a promising assistive technology for individuals with cognitive deficits who live in supported community residences.Implication For RehabilitationCOOK is a promising assistive technology for cognition to increase independence and safety in meal preparation for clients with ABI within their supported living contexts.Receiving training from an expert and the availability of technical support are imperative to the successful adoption of COOK.
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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 | 0.004 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".