Potential of using an assistive technology to address meal preparation difficulties following acquired brain injury: clients’ and caregivers’ perspectives
Bibliographic record
Abstract
This study explored difficulties in meal preparation experienced by adults with moderate to severe acquired brain injury (ABI) and available compensatory strategies from both ABI individuals’ and caregivers’ perspectives. Further, this study investigated their opinions on potential benefits, barriers and facilitators to the use of the Cognitive Orthosis for coOKing (COOK) in their living environment. Using a qualitative descriptive approach, semi-structured individual interviews and focus groups were carried out with adults with moderate to severe ABI (n = 20) and formal and informal caregivers (n = 13) in Ontario and Quebec, Canada. A qualitative analysis based on Miles et al.’s approach was used. According to participants, cognitive, physical, psychosocial dysfunctions and lack of availability of supportive caregivers were the main difficulties that impede persons with ABI from engaging effectively in meal preparation tasks. Memory aids on smartphones, and caregivers’ direct support were reported as the most commonly used compensatory strategies, though the latter do not provide adequate support. COOK was identified as a technology with great potential to improve independence and increase safety in meal preparation for these clients while decreasing caregiver burden. However, psychosocial issues and limited access to funding were considered as the main barriers to the use of COOK. Providing training and the availability of financial support were mentioned as the main facilitators to the use of this technology. Findings of this study on difficulties of meal preparation following ABI and potential benefits and barriers of COOK will help improve this technology and customize it to the needs of clients with ABI and their caregivers.Implications for RehabilitationCurrent compensatory strategies are not tailored to the specific needs of clients with ABI and cannot provide sufficient support for caregivers.COOK shows a high potential for increasing independence and safety during meal preparation in a living environment for clients with ABI via a sensor-based autonomous safety system and a cognitive assistance application.COOK has the potential to decrease caregivers’ burden by proving remote access to a stove/oven. Current compensatory strategies are not tailored to the specific needs of clients with ABI and cannot provide sufficient support for caregivers. COOK shows a high potential for increasing independence and safety during meal preparation in a living environment for clients with ABI via a sensor-based autonomous safety system and a cognitive assistance application. COOK has the potential to decrease caregivers’ burden by proving remote access to a stove/oven.
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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 | 0.003 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".