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Record W2027787146 · doi:10.5014/ajot.2014.008987

Self-Generated Strategic Behavior in an Ecological Shopping Task

2013· article· en· W2027787146 on OpenAlexaff
Carolina Bottari, Priscilla Lam Wai Shun, Guylaine Le Dorze, Nadia Gosselin, Deirdre Dawson

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoHôpital du Sacré-Cœur de MontréalUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTask (project management)RepertoireCognitionPsychologyActivities of daily livingIndependence (probability theory)Exploratory researchApplied psychologyEcological validityCognitive psychologyPsychiatrySociologyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVES. The use of cognitive strategies optimizes performance in complex everyday tasks such as shopping. This exploratory study examined the cognitive strategies people with traumatic brain injury (TBI) effectively use in an unstructured, real-world situation. METHOD. A behavioral analysis of the self-generated strategic behaviors of 5 people with severe TBI using videotaped sessions of an ecological shopping task (Instrumental Activities of Daily Living Profile) was performed. RESULTS. All participants used some form of cognitive strategy in an unstructured real-world shopping task, although the number, type, and degree of effectiveness of the strategies in leading to goal attainment varied. The most independent person used the largest number and a broader repertoire of self-generated strategies. CONCLUSION. These results provide initial evidence that occupational therapists should examine the use of self-generated cognitive strategies in real-world contexts as a potential means of guiding therapy aimed at improving independence in everyday activities for people with TBI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.193
GPT teacher head0.428
Teacher spread0.235 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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