The WHO ICF comprehensive Core Set for deafblindness: A narrative overview of the development process
Bibliographic record
Abstract
The World Health Organization’s (WHO) International Classification of Functioning, Disability, and Health (ICF) is a framework designed to describe and measure health and disability. It examines how a person’s health condition affects their daily life and overall functioning and considers not just the physical or mental limitations that may come from a condition but also how environmental factors (like accessibility and social support) influence a person’s ability to participate in life. An ICF Core Set is a short list of the most important factors to consider when looking at how a specific health condition affects a person’s life. These Core Sets are made to be practical tools for health care providers, researchers, and caregivers. Here we describe how this process was conducted for the development of the comprehensive Core Set for deafblindness. The four required studies included a systematic literature review, qualitative interviews with individuals living with deafblindness, an online expert survey, and a multi-centered clinical evaluation. All studies include data from each of the six regions of the WHO (Africa, the Americas, Europe, the Eastern Mediterranean, the Western Pacific, and South-East Asia). Data from 54 countries were linked using the ICF coding system, then merged, and presented at an international consensus conference in Spain in 2024. The process resulted in the first version of the comprehensive Core Set for deafblindness with 218 codes representing the most salient functional effects of deafblindness. This Core Set builds the foundation for an eventual brief Core Set (~30 codes), and allows us to begin the process of validation and implementation in policy, teaching, research, and service delivery environments.
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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 | 0.078 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".