WHO Atlas on Global Resources for Persons with Intellectual Disabilities 2007: Key Findings Relevant for Low‐ and Middle‐Income Countries
Bibliographic record
Abstract
Abstract The World Health Organization (WHO) Atlas‐ID project was designed to collect, compile, and disseminate information on intellectual disabilities (ID) services and resources from across the world. This paper aims at selecting findings in the Atlas‐ID that can be used as a tool for advocacy, human rights awareness, development planning, and monitoring changes regarding resources for persons with intellectual disabilities and their families in countries with the lowest levels of income in the world. After consultation with experts in the field of ID, a questionnaire and its accompanying glossary were developed. This questionnaire was completed by national respondents from 147 countries, areas, and territories that are WHO members (response rate of 74.6% corresponding to 94.6% of the world population). Cross‐tabulations were calculated according to WHO region that the countries belong to as well as their levels of income. The data from the Atlas‐ID allowed for documenting similarities and differences among the poorest and the richest countries of the world in relation to ID. The most striking differences pertain to the areas of information, judicial protection, government benefits, financing, availability, and access to services. The Atlas‐ID allowed the identification of similarities and differences in resources and services between the four World Bank categories of countries income, and it demonstrated the extent of unmet needs in low‐income and low‐middle‐income countries, as well as some critical gaps between these countries and the high‐level income countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.041 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".