Framing international trade and chronic disease
Bibliographic record
Abstract
There is an emerging evidence base that global trade is linked with the rise of chronic disease in many low and middle-income countries (LMICs). This linkage is associated, in part, with the global diffusion of unhealthy lifestyles and health damaging products posing a particular challenge to countries still facing high burdens of communicable disease. We developed a generic framework which depicts the determinants and pathways connecting global trade with chronic disease. We then applied this framework to three key risk factors for chronic disease: unhealthy diets, alcohol, and tobacco. This led to specific 'product pathways', which can be further refined and used by health policy-makers to engage with their country's trade policy-makers around health impacts of ongoing trade treaty negotiations, and by researchers to continue refining an evidence base on how global trade is affecting patterns of chronic disease. The prevention and treatment of chronic diseases is now rising on global policy agendas, highlighted by the UN Summit on Noncommunicable Diseases (September 2011). Briefs and declarations leading up to this Summit reference the role of globalization and trade in the spread of risk factors for these diseases, but emphasis is placed on interventions to change health behaviours and on voluntary corporate responsibility. The findings summarized in this article imply the need for a more concerted approach to regulate trade-related risk factors and thus more engagement between health and trade policy sectors within and between nations. An explicit recognition of the role of trade policies in the spread of noncommunicable disease risk factors should be a minimum outcome of the September 2011 Summit, with a commitment to ensure that future trade treaties do not increase such risks.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".