Die Relevanz von Netzwerkarbeit in der schulischen Gesundheitsförderung
Bibliographic record
Abstract
School is one of the most important places to start promoting the health of children and young people, partly because pupils spend a large part of their time here. However, school can only be partially dedicated to health promotion, and school itself is only part of the everyday environment by which health behaviour is shaped. In order to increase the effectiveness of health promotion, the formation of networks between school and municipal actors such as sports clubs, youth welfare services, counselling centres and health authorities seems to make sense.This article addresses the question of the relevance of networks in the context of school health promotion. The derivation is on the one hand based on the legal framework of the educational mandate of schools and the so-called Prevention Act of 2015 and on the other hand on the Ottawa Charter of the World Health Organization (WHO) as well as on the discourse developed from it around the terms "health promotion" and "setting approach". Perspectives are shown on how networks can be designed in a scientifically sound way and how suitable network partners can be won. Possible risks and opportunities of networking are analysed, and factors of success and failure are pointed out.Networking should be obligatory for schools in terms of health promotion. It can help identify needs within the school and at the same time be a key in dealing with resulting challenges. Networking requires above all the motivation of the actors. A common vision, fixed structures, continuity and appropriate personnel considerations contribute to the success of the cooperation.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".