What Generates Attention to Health in Trade Policy-Making? Lessons From Success in Tobacco Control and Access to Medicines: A Qualitative Study of Australia and the (Comprehensive and Progressive) Trans-Pacific Partnership
Bibliographic record
Abstract
BACKGROUND: Despite greater attention to the nexus between trade and investment agreements and their potential impacts on public health, less is known regarding the political and governance conditions that enable or constrain attention to health issues on government trade agendas. Drawing on interviews with key stakeholders in the Australian trade domain, this article provides novel insights from policy actors into the range of factors that can enable or constrain attention to health in trade negotiations. METHODS: A qualitative case study was chosen focused on Australia's participation in the Trans-Pacific Partnership (TPP) negotiations and the domestic agenda-setting processes that shaped the government's negotiating mandate. Process tracing via document analysis of media reporting, parliamentary records and government inquiries identified key events during Australia's participation in the TPP negotiations. Semi-structured interviews were undertaken with 25 key government and non-government policy actors including Federal politicians, public servants, representatives from public interest nongovernment organisations and industry associations, and academic experts. RESULTS: Interviews revealed that domestic concerns for protecting regulatory space for access to generic medicines and tobacco control emerged onto the Australian government's trade agenda. This contrasted with other health issues like alcohol control and nutrition and food systems that did not appear to receive attention. The analysis suggests sixteen key factors that shaped attention to these different health issues, including the strength of exporter interests; extent of political will of Trade and Health Ministers; framing of health issues; support within the major political parties; exogenous influencing events; public support; the strength of available evidence and the presence of existing domestic legislation and international treaties, among others. CONCLUSION: These findings aid understanding of the factors that can enable or constrain attention to health issues on government trade agendas, and offer insights for potential pathways to elevate greater attention to health in future. They provide a suite of conditions that appear to shape attention to health outside the biomedical health domain for further research in the commercial determinants of health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".