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Record W2113323062 · doi:10.3109/17483107.2015.1029536

Identifying participation needs of people with acquired brain injury in the development of a collective community smart home

2015· article· en· W2113323062 on OpenAlexaff
Mélanie Levasseur, Hélène Pigot, Mélanie Couture, Nathalie Bier, Bonnie Swaine, Pierre-Yves Thérriault, Sylvain Giroux

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité du Québec à Trois-RivièresCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalCentre de Santé et de Services Sociaux de ChicoutimiInstitut Universitaire de Gériatrie de MontréalCentre de Santé et de Services Sociaux CavendishHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsAcquired brain injuryPhysical medicine and rehabilitationInternet privacyBusinessPsychologyMedicineComputer scienceRehabilitationNeuroscience

Abstract

fetched live from OpenAlex

PURPOSE: This study explored the personalized and collective participation needs of people with acquired brain injury (ABI) living in a future shared community smart home. METHODS: An action research study was conducted with 16 persons, seven with ABI, four caregivers and five rehabilitation or smart home healthcare providers. Twelve interviews and two focus groups were conducted, audiotaped, transcribed and analyzed for content. RESULTS: Seventy personalized and 18 collective participation needs were reported related to daily and social activities. Personalized needs concerned interpersonal relationships, general organization of activities, leisure, housing, fitness and nutrition. Collective needs related mainly to housing, general organization of activities and nutrition. CONCLUSIONS: Personalized and collective participation needs of people with ABI planning to live in a community smart home are diverse and concern daily as well as social activities. Implications for Rehabilitation To meet participation needs of people with ABI, the design of smart homes must consider all categories of daily and social activities. Considering personalized and collective needs allowed identifying exclusive examples of each. As some persons with ABI had difficulty identifying their needs as well as accepting their limitations and the assistance required, rehabilitation professionals must be involved in needs identification.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.081
GPT teacher head0.377
Teacher spread0.296 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations20
Published2015
Admission routes1
Has abstractyes

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