Systematic Literature Review on ICF From 2001 to 2013 in the Nordic Countries Focusing on Clinical and Rehabilitation Context
Bibliographic record
Abstract
We present a systematic review on International Classification of Functioning, Disability and Health (ICF) used in the Nordic countries from 2001 through 2013, describing and quantifying the development in utilization of ICF, and describe the extent to which the different components of the ICF have been used. A search was conducted in EMBASE, MEDLINE and PsycInfo. Papers from Nordic countries were included if ICF was mentioned in title or abstract. Papers were assigned to one of eight categories covering the wide rehabilitation area; furthermore, area of focus was assigned. Use of ICF components and intervention were coded in papers categorized as "clinical and/or rehabilitation contexts" or "non-clinical contexts". One hundred seventy papers were included, of these 99 papers were from the categories "clinical and/or rehabilitation contexts" or "non-clinical contexts". Forty-two percent of the 170 included papers were published in the period 2011 - 2013. There was an increase in ICF-relevant papers from 2001 to 2013, especially in the categories "clinical and/or rehabilitation contexts" and "non-clinical contexts". The most represented focus areas were neurology, musculoskeletal, and work-related areas. All five or at least four ICF components were mentioned in the results or discussions in most papers, and activity was most frequently mentioned.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".