The activities and participation categories of the ICF Core Sets for multiple sclerosis from the patient perspective
Bibliographic record
Abstract
Purpose: To validate the activities and participation components of The International Classification of Functioning, Disability and Health (ICF).Methods: In this cross-sectional study, 113 Finnish community-dwelling persons with MS were assessed using a semi-structured interview provided by the Canadian Occupational Performance Measure (COPM) to capture participants’ self-perceived problems in everyday activities and participation. Problems were linked to the ICF categories.Results: Participants identified 527 of the most important occupational performance problems. They covered all chapters of the ICF Activities and Participation components. Forty-one categories out of a total 53 ICF activities and participation categories of the Comprehensive ICF Core Set and four out of five categories of the Brief ICF Core Set were reported on by the participants. The most common category in this sample, ‘d920 Recreation and leisure’ (145 problems/27.5%), is not included in the Brief ICF Core Set.Conclusions: Most, but not all, ICF activities and participation categories of the ICF Core Sets for MS could be confirmed from the perspective of persons with MS. It is worth considering to add category ‘d920 Recreation and leisure’ to the Brief ICF Core Set.Implications for RehabilitationThe perceived problems of persons with MS support current versions of the ICF Core Sets for MS.The subjective experiences of prioritized problems encountered in everyday life vary considerably among community-dwelling persons with MS.Persons with MS often experience problems with recreation and leisure activities.Experiences of patient about recreation and leisure activities should be asked more systematically during rehabilitation process and the role of recreation and leisure should be considered when further developing the Brief ICF Core Sets for MS.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".