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Record W2515410652 · doi:10.1177/0308022616658298

Results from a cognitive group rehabilitation programme from an occupational performance perspective

2016· article· en· W2515410652 on OpenAlexaboutno aff
Birgitta Rustner, Ewa Wressle, Kersti Samuelsson

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthOccupational therapyRehabilitationPerspective (graphical)Physical medicine and rehabilitationCore (optical fiber)PsychologyPhysical therapyCognitionSet (abstract data type)MedicineClinical psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Introduction The Canadian Occupational Performance Measure (COPM) was used for treatment planning and to evaluate the effect of a cognitive group rehabilitation programme. The aim was to identify occupational performance problems defined as important and to analyse the outcome, and to link those problems to the International Classification of Functioning, Disability and Health (ICF) core set for traumatic brain injury and stroke. Method A retrospective design was used, including an analysis of COPM data recorded before and two months after the programme. COPM data from 124 clients were linked to the ICF core sets. Results A clinically important difference of ≥2 COPM scores was reached in 32% of the clients for occupational performance and in 47% for satisfaction with occupational performance. A majority of the problems identified (62%) were classified within the activities and participation component in the ICF, and 38% in body functions. All occupational performance problems could be linked to the ICF; just one of the 36 categories (caring for household objects) was not found in any of the ICF core sets. Conclusion By linking the COPM data to the core sets, occupational therapists can be confident in addressing the typical problems of the group of clients identified.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.341
Teacher spread0.294 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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