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Record W3128605556 · doi:10.1080/17483107.2020.1867244

Potential of using an assistive technology to address meal preparation difficulties following acquired brain injury: clients’ and caregivers’ perspectives

2021· article· en· W3128605556 on OpenAlexafffundabout
Sareh Zarshenas, Mireille Gagnon‐Roy, Mélanie Couture, Nathalie Bier, Sylvain Giroux, Emily Nalder, Hélène Pigot, Deirdre Dawson, Frédérique Poncet, Guylaine LeDorze, Carolina Bottari

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2021
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre de réadaptation Lethbridge-Layton-MackayBaycrest HospitalToronto Rehabilitation InstituteMarch of Dimes CanadaInstitut Universitaire de Gériatrie de MontréalUniversité de SherbrookeUniversité de MontréalUniversity Health NetworkUniversity of TorontoCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsAssistive technologyAcquired brain injuryMeal preparationPsychologyMealPhysical medicine and rehabilitationMedicineGerontologyPhysical therapyComputer scienceRehabilitationHuman–computer interactionFood science

Abstract

fetched live from OpenAlex

OBJECTIVES: 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. METHODS: = 13) in Ontario and Quebec, Canada. A qualitative analysis based on Miles et al.'s approach was used. RESULTS: 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. CONCLUSIONS: 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.372
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2021
Admission routes3
Has abstractyes

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