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Record W2013925427 · doi:10.1080/09602011.2014.971819

Reliability of the Cooking Task in adults with acquired brain injury

2014· article· en· W2013925427 on OpenAlexaff
Frédérique Poncet, Bonnie Swaine, C. Taillefer, Julie Lamoureux, P. Pradat-Diehl, Mathilde Chevignard

Bibliographic record

VenueNeuropsychological Rehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsReliability (semiconductor)PsychologyTask (project management)Physical medicine and rehabilitationAcquired brain injuryRehabilitationAudiologyMedicineClinical psychologyNeuroscienceEngineering

Abstract

fetched live from OpenAlex

Acquired brain injury (ABI) often leads to deficits in executive functioning (EF) responsible for severe and long-standing disabilities in daily life activities. The Cooking Task is an ecological and valid test of EF involving multi-tasking in a real environment. Given its complex scoring system, it is important to establish the tool's reliability. The objective of the study was to examine the reliability of the Cooking Task (internal consistency, inter-rater and test-retest reliability). A total of 160 patients with ABI (113 men, mean age 37 years, SD = 14.3) were tested using the Cooking Task. For test-retest reliability, patients were assessed by the same rater on two occasions (mean interval 11 days) while two raters independently and simultaneously observed and scored patients' performances to estimate inter-rater reliability. Internal consistency was high for the global scale (Cronbach α = .74). Inter-rater reliability (n = 66) for total errors was also high (ICC = .93), however the test-retest reliability (n = 11) was poor (ICC = .36). In general the Cooking Task appears to be a reliable tool. The low test-retest results were expected given the importance of EF in the performance of novel tasks.

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)0.005
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 score0.076
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.275
Teacher spread0.269 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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