Gesundheitliche Chancengleichheit in der digitalen Gesundheitsförderung und Prävention am Beispiel des Settings Schule
Bibliographic record
Abstract
0. Einleitung 154 1. Wechselwirkungen gesundheitlicher, sozialer und digitaler Ungleichheiten in Bezug zum Settingansatz 155 1.1 Soziokonomischer Status und Gesundheit 155 1.2 Unterschiedliche Auswirkungen von (digitalen) Gesundheitsinterventionen 156 1.3 Settingbasierte Anstze zur (digitalen) Gesundheitsfrderung und Prvention 158 2. Digitale Technologien in der Gesundheitsfrderung und Prvention 159 3. Beleuchtung von Ungleichheiten in der digitalen Gesundheitsfrderung und Pr vention am Beispiel des Settings Schule 160 3.1 Bedeutung des Settings Schule fr Gesundheitsfrderung und Prvention und die digitalen Gesundheitskompetenz bei Schler*innen und Lehrpersonal 160 3.2 Stand der Digitalisierung im Setting Schule 161 3.3 Digitale Technologien im Setting Schule und deren Verbreitung 162 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.023 |
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; both teacher heads agree on what is shown here.
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".