Impact of obesity policies on health and the economy
Bibliographic record
Abstract
This chapter presents results from modelling the implementation of ten policy actions including food labelling; menu labelling; mass media campaigns promoting physical activity; prescribing physical activity in primary care; mobile apps promoting healthy lifestyles; workplace wellness programmes; workplace sedentary behaviour programmes; school-based programmes; expanded public transport and statutory bans on advertising targeting children. In addition, the impact of three policy packages is shown, including a package of mostly existing, communication-based policies; a package of physical activity-based policies; as well as a mixed package of policies that are still relatively rarely implemented in OECD countries, but nevertheless show significant promise. Results are presented for 36 countries, including OECD countries in the European Region as well as Japan, Mexico, Canada, and Australia, together with other non-OECD EU28 member states and South Africa. A particularly innovative aspect of this analysis is its focus not only on health outcomes, but also on economic outcomes, including the policy impact on health spending, on the employment and productivity of workers, as well as on the gross domestic product (GDP) of countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".