The Trans-Pacific Partnership Agreement and health: few gains, some losses, many risks
Bibliographic record
Abstract
BACKGROUND: In early October 2015, 12 nations signed the Trans-Pacific Partnership Agreement (TPPA), promoted as a model '21(st) century' trade and investment agreement that other countries would eventually join. There are growing concerns amongst the public health community about the potential health implications of such WTO+ trade and investment agreements, but little existing knowledge on their potential health impacts. METHODS AND RESULTS: We conducted a health impact review which allows for a summary estimation of the most significant health impacts of a set of policies, in our case the TPPA. Our analysis shows that there are a number of potentially serious health risks, with the following key pathways linking trade to health: access to medicines, reduced regulatory space, investor-state dispute settlement (ISDS), and environmental protection and labor rights. We also note that economic gains that could translate into health benefits will likely be inequitably distributed. CONCLUSION: Our analysis demonstrates the need for the public health community to be knowledgeable about trade issues and more engaged in trade negotiations. In the context of the COP21 climate change Agreement, and the UN Sustainable Development Goals, this may be an opportune time for TPPA countries to reject it as drafted, and rethink what should be the purpose of such agreements in light of (still) escalating global wealth inequalities and fragile environmental resources-the two most foundational elements to global health equity.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".