Noncommunicable diseases, risk factors, commercial determinants
Bibliographic record
Abstract
Unhealthy diet is an important risk factor for chronic diseases. Often this is seen as an individual level problem, but what we eat to a large extent is determined by complex, external factors, that lie outside the direct scope of control of individuals. In this plenary, the link between the commercial determinants of health, diet, and chronic diseases will be explored with a focus on positive action and what can be done concretely to improve our food systems and diets and thereby our health. The topic will be addressed from several angles, looking e.g., at what policy and legislation can achieve, how civil society can contribute, and how we can empower individuals through improved health literacy. Moderators: Josep Figueras, Director European Observatory on Health Systems and Policies Nicole Mauer, European Observatory on Health Systems and Policies Introductory keynote speaker: Iveta Nagyova, President EUPHA and Head of the Department of Social and Behavioural Medicine, Faculty of Medicine, PJ Safarik University, Kosice, Slovakia Speakers/Panellists: Mark Petticrew, London School of Hygiene and Tropical Medicine, London, UK Amandine Garde, Public Health and School of Law and Social Justice, University of Liverpool, Liverpool, UK Simon Bacon, Concordia University, Montreal, Canada
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".