Community integration following TBI: An examination of community integration measures within the ICF framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".