Proceedings of the International Workshop ‘Integration of International Expertise in the Development of a Mental Health Surveillance System in Germany’
Bibliographic record
Abstract
In 2019, the Robert Koch Institute (RKI) as the national Public Health Institute in Germany was commissioned by the Federal Ministry of Health to develop a concept for continuous health reporting on mental health in Germany. Meaningful data is required since mental health has strong public health relevance due to high prevalence and burden of psychological distress and mental disorders and the improvable care situation. Furthermore there is still unexploited potential to promote positive mental health. In Germany, almost one in three adults (27.8%) fulfils the criteria of a mental disorder within one year [1, 2]; the prevalence of emotional and behavioral disorders in children and adolescents is estimated at 10 to 20% [3]. Depressive disorder ranks globally as well as nationally among the most significant causes of Years Lost due to Disability (YLDs) [4, 5]. Additionally physical and mental health is closely interwoven: mental disorders deteriorate the course of somatic illnesses and vice versa, somatic illnesses represent a risk factor for the emergence of mental disorders. Against this background, the WHO has included mental disorders in its list of central non-communicable diseases [6]. Moreover, the establishment of Mental Health Information Systems is one of the four priority objectives of the WHO's Mental Health Action Plan. Besides mental disorders, this action plan targets on mental well-being as an integral component of health in general [7].
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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 | 0.061 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".