Two decades of the International Classification of Functioning, Disability and Health (ICF) in health research: a bibliometric analysis
Bibliographic record
Abstract
: We conducted a twenty-year bibliometric analysis of scientific literature, focusing on the trends of The International Classification of Functioning, Disability and Health (ICF) use in health research. : We retrieved 3'467 documents published between 2002 and 2022, sourced from the Web of Science Core Collection database. We used the Bibliometrix and VoSviewer tools for descriptive analyses and data visualization. : Our findings indicate a significant increase in ICF application since 2011, with an average annual growth rate of 13.19%. Prominent contributions were observed globally, with notable outputs from the U.S., Canada, Germany, the Netherlands, and Switzerland. The Ludwig Maximilian University Munich, Swiss Paraplegic Research, and McMaster University authored a quarter of the documents (24.6%). Collaboration networks of countries and institutions revealed robust partnerships, particularly between Germany and Switzerland. "Rehabilitation" was the most frequently occurring keyword, although a thematic shift towards epidemiology, aging, and health-related quality of life was observed post-2020. While rehabilitation remained the primary thematic focus, literature post-2020 highlighted epidemiology as a growing area of interest. : A steady increase in ICF-based research mirrors the rising interest in a biopsychosocial and person-centered approach to healthcare. However, the literature is primarily produced by high-resource countries, with underrepresentation from low and middle-resource countries, suggesting an area of future research to address this discrepancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.041 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".