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Record W7065981059

Functional Cognitive Activities for Adults with Traumatic Brain Injury: Pilot Case Studies

2017· article· en· W7065981059 on OpenAlexaboutno aff

Bibliographic record

VenueDominican Scholar (Dominican University of California) · 2017
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryCognitionActivities of daily livingGeneralizationIntervention (counseling)Cognitive trainingAcquired brain injuryCognitive skillCognitive rehabilitation therapy
DOInot available

Abstract

fetched live from OpenAlex

These pilot case studies investigated the effectiveness of the Functional Cognitive Activities for Adults with Brain Injury: A Sequential Approach (FCA) in generalizing functional cognitive skills across meaningful occupations for adults with traumatic brain injury (TBI). This quasi-experimental pretest-posttest design consisted of two participants with TBI. Both participants received occupation-based intervention sessions twice a week and equaled a total of 14 sessions each. For pretest, the two participants were given three assessments to track changes with aspects of functional cognition and engagement in occupations: the Canadian Occupational Performance Measure (COPM), Kohlman Evaluation of Living Skills (KELS), and Goal Attainment Scale (GAS). After completing the treatment sessions, the participants completed the COPM, KELS, and GAS as posttest measures. Four months later, the COPM and the GAS were administered along with a brief phone interview to determine if generalization of strategies to overcome cognitive deficits has occurred. Findings from this study provide preliminary evidence supporting the effectiveness of the FCA approach in improving functional cognitive skills and generalizability of skills to novel tasks in individuals 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.104
GPT teacher head0.369
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations0
Published2017
Admission routes1
Has abstractyes

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