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Record W2168127736 · doi:10.3109/02699052.2011.613088

Community integration following TBI: An examination of community integration measures within the ICF framework

2011· article· en· W2168127736 on OpenAlexaff
Katherine Salter, J. Andrew McClure, Norine Foley, Robert Teasell

Bibliographic record

VenueBrain Injury · 2011
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern UniversityLawson Health Research Institute
Fundersnot available
KeywordsCommunity integrationInternational Classification of Functioning, Disability and HealthPsychologyApplied psychologyInclusion (mineral)PopulationClinical psychologyProcess (computing)MedicineRehabilitationPhysical therapyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

PURPOSE: The objectives of the present study are (1) to examine whether the content of existing community integration measures used following traumatic brain injury (TBI) is represented in the International Classification of Functioning, Disability and Health (ICF) and (2) to determine if the ICF provides a reasonable framework within which such measurement tools may be compared. METHOD: Five commonly-used assessment instruments were selected for inclusion. Independent raters mapped identified measurement concepts to the ICF using established linking rules. RESULTS: One hundred and eighty-five concepts were identified from 85 items in five scales. Of these more than 75% could be linked to the ICF. The majority of linked concepts were assigned to 64 categories within the activities and participation component of the ICF; however, the focus of assessment within each instrument varied considerably. CONCLUSION: Through a standardized process of item mapping to the ICF, one may examine operationalizations of community integration. This may help inform selection of a method of assessment appropriate to both the subject population and clinical or research purpose. However, this process allows comparison of only the objective content of measurement tools. Subjective evaluations may also be necessary to provide comprehensive assessment of community integration.

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)0.011
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.207
GPT teacher head0.386
Teacher spread0.179 · 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

Citations18
Published2011
Admission routes1
Has abstractyes

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