Settingbezogene Gesundheitsförderung und Prävention in der digitalen Transformation
Bibliographic record
Abstract
Our everyday lives, which are largely shaped in particular settings, are being increasingly affected by technological innovations. For example, work processes and organisational structures are becoming increasingly digital, which creates new health opportunities but also poses risks. From the perspective of science, practice and politics, this book reflects on and discusses what the digital transformation of settings as well as the availability of new digital tools mean for setting-related health promotion and prevention. Against this background, a new conceptual understanding of digital settings is presented in the context of the setting approach. This book presents the latest research findings, practice-based projects and current professional experiences. <bold>With contributions by</bold> Joanna Albrecht | Jennifer Apolinário-Hagen | Anja Bestmann | Berit Brandes | Dirk Bruland | Heide Busse | Julia Anna Deipenbrock | Christoph Dockweiler | Gudrun Faller | Susanne Giel | Ludwig Grillich | Beate Grossmann | Rahim Hajji | Stephanie M. Helmer | Laura Herrera Bayo | Friederike Keipke | Jessica Kemper | Marion Kiem | Lena Köhler | Kilian Krämer | Simon Lang | Änne-Dörte Latteck | Matthias Meyer | Kristin Mielke | Markus Möckel | Saskia Muellmann | Eva Obernauer | Nadine Pieck | Uwe Prümel-Philippsen | Inke Ruhe | Christel Salewski| Philip Santangelo | Ulrike Scorna | Mariella Seel | Jelena Sörensen | Anna Lea Stark | Elitsa Uzunova | Gunnar Voß | Stefan Winter
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".