20 Years of ICF—International Classification of Functioning, Disability and Health: Uses and Applications around the World
Bibliographic record
Abstract
The International Classification of Functioning Disability and Health (ICF) was approved in 2001 and, since then, several studies reported the increased interest about its use in different sectors. A recent overview that summarizes its applications is lacking. This study aims to provide an updated overview about 20 years of ICF application through an international online questionnaire, developed by the byline authors, and sent to each World Health Organization Collaborating Centers of the Family of International Classifications (WHO-FIC CCs). Data was collected during October 2020 and December 2021 and descriptive content analyses were used to report main results. Results show how, in most of the respondent countries represented by WHO-FIC CCs, ICF was mainly used in clinical practice, policy development and social policy, and in education areas. Despite its applications in different sectors, ICF use is not mandatory in most countries but, where used, it provides a biopsychosocial framework for policy development in health, functioning and disability. The study provides information about the needs related to ICF applications, that can be useful to organize targeted intervention plans. Furthermore, this survey methodology can be re-proposed periodically to monitor the use of the ICF in the future.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".