Potential advantages, barriers, and facilitators of implementing a cognitive orthosis for cooking for individuals with traumatic brain injury: the healthcare providers’ perspective
Bibliographic record
Abstract
Considering the key role of health care providers in integrating assistive technologies into clinical settings (e.g., in/outpatient rehabilitation) and home, this study explored the care providers’ perspectives on benefits, barriers and facilitators to the implementation of the Cognitive Orthosis for coOking (COOK) for adults with traumatic brain injury (TBI) within clinical contexts and homes. Using a qualitative descriptive approach, semi-structured individual interviews and focus groups were carried out with experienced care providers of adults with TBI (n = 30) in Ontario-Canada. Qualitative analysis based on the Miles et al approach was used. According to the participants, COOK could potentially be used with individuals with cognitive impairments (TBI and non-TBI) to increase safety and independence in meal preparation and support healthcare providers. However, limited access to funding, clients’ lack of motivation/knowledge, and the severity of their cognitive and motor impairments were perceived as potential barriers. Facilitators to the use of COOK include training sessions, availability of private/provincial financing, and comprehensive assessments by a clinical team prior to use. Health care providers’ perspectives will help develop implementation strategies to facilitate the adoption of COOK within homes and clinical contexts for individuals with TBI and improve the next version of this technology.IMPLICATIONS FOR REHABILITATIONCOOK shows a high potential for increasing independence and safety during meal preparation with its sensor-based monitoring of the environment and cognitive-based assistance, for adults with TBI.Comprehensive clinical assessments to identify individuals’ therapeutic goals, clinical characteristics, and living environments are necessary to facilitate the deployment of COOK. COOK shows a high potential for increasing independence and safety during meal preparation with its sensor-based monitoring of the environment and cognitive-based assistance, for adults with TBI. Comprehensive clinical assessments to identify individuals’ therapeutic goals, clinical characteristics, and living environments are necessary to facilitate the deployment of COOK.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".