The WHO ICF comprehensive Core Set for deafblindness: A narrative overview of the development process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it