Förderung der psychischen Gesundheit und Prävention psychischer Störungen: Gibt es bevölkerungsbezogene Konzepte? Ein Blick ins Ausland
Bibliographic record
Abstract
BACKGROUND: The rising public health relevance of mental health requires increased activities in the field of mental health promotion and prevention. In recent times interest in this sphere increases in Germany. OBJECTIVE: Which kinds of population-based mental health promotion programmes and prevention approaches are internationally available? What can be learned from their example? METHOD: Systematic search of the Internet and literature databases. Analysis and comparison of the available mental health promotion and prevention concepts with regard to targets, sub-targets, target groups, protagonists, settings, outcome indicators and evidence management. RESULTS: Concepts for mental health promotion and prevention programmes exist in England, Australia, the European Union and in Quebec, Canada. The concepts show similarities such as choosing similar settings like work place and schools or similar target groups like caretakers or adults in particular challenging life circumstances. CONCLUSIONS: Concepts for population-based mental health promotion and prevention have been created in some countries. For developing such programmes in Germany one should refer to internationally available experience as well as to own gathered experiences as to own expressions gathered pilot projects and the German Health Target Programme.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".