ICD-11 Revision of Mental Disorders: the Global Standard for Health Data, Clinical Documentation, and Statistical Aggregation
Bibliographic record
Abstract
Mental health conditions in the World Health Organization (WHO) European Region affect more than 10% of the population, with 140,000 lives lost annually to suicide. Comorbidity with other diseases is high. However, basic mental health care is received by less than a third of patients. The COVID-19 pandemic has revealed the vulnerability of mental health services to disruptions and underscored the need to integrate mental health into response strategies. One of the flagship initiatives of the WHO European Programme of Work (EPW), 20202025: United Action for Better Health in Europe is the establishment of a Mental Health Coalition at the European level. In this framework, reporting of health statistics using the International Classification of Diseases 11th Revision (ICD-11) will begin on 1st January 2022. Clinical utility, scientific rigour and wider cultural applicability were all of prime importance in the development of the ICD-11. The 11th Revision was the end product of the most extensive global, multilingual, multidisciplinary and participative process ever undertaken for this task, involving more than 15,000 experts from 155 countries, representing approximately 80% of the worlds population. With the adoption of the ICD-11 and the priority being given to mental health, new ideas based on the 30 years of research since the approval of the ICD-10 will be widely adopted and applied.
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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 | 0.024 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.018 |
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".