Digital Public Health – Hebel für Capacity Building in der kommunalen Gesundheitsförderung
Bibliographic record
Abstract
In 1986, the Ottawa charter marked a paradigm shift for public health, putting the focus on strengthening community action and on creating supportive environments for health. A key to this is "capacity building" (CB), which we understand as the development and sustainable implementation of structural capacities, e.g. coordinated data collection, collaboration processes across sectors and reliable provision of basic resources in all areas of local health promotion.Many efforts and three and a half decades later we still envisage infrastructure deficits, scattered public health landscapes and restraints to intersectoral cooperation much too often. While agreement on the theoretical insights on what is needed appears to be broad, translating these insights into practice remains a challenge. In this situation, digital public health (DPH) can contribute to overcoming barriers and making knowledge for action more visible and more accessible. With DPH, data can be integrated, structured and disseminated in novel ways.We discuss why CB at the local level could benefit from technological advances and what DPH might do for the provision of information services on public health capacity. Our focus is on the web-based, interactive representation of public health data for use in information, governance or benchmarking processes. As an example from public health practice, the Finnish tool TEAviisari (National Institute for Health and Welfare, Finland) is presented.The 2020 EU Council Presidency of Germany - with the topics of digitalisation and the common European health data space - offers opportunities to decisively advance the development of CB in health promotion in this country.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".