Promoting health through personal change in social networks: A German–Danish partnership
Bibliographic record
Abstract
The project Healthy in Everyday Life is a German–Danish partnership between local health promoters and researchers from the European University of Flensburg, Germany. The objective was to promote health opportunities at the local level by qualifying citizens as health mediators, who then become active in their neighbourhoods. It was implemented in the Danish municipalities of Sønderborg and Aabenraa and the German city of Flensburg. The project processes were evaluated using participatory research methods. The project partners worked together transnationally on all stages of the project, from the recruitment of participants, to training, the development of the evaluation design and the appraisal of evaluation results. The evaluation consisted of three levels: (1) health changes on an individual level for participants; (2) impact on social environments and neighbourhoods; and (3) the transnational collaboration. This paper presents selected results. Positive developments in the health-related behaviour of the training participants were recorded. Primary networks, such as family relationships, were shown to be supportive resources. It was not possible to determine any impact on the neighbourhoods. The transnational collaboration was perceived as enriching. At the same time, there were challenges in involving the health professionals in the evaluation process, such as restricted time for joint reflection and a lack of research skills in the community practitioners. In conclusion, the project was successful in developing a health-promoting approach that received a strong response in the German and Danish municipalities involved.
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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 | 0.022 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".