Factors influencing the prioritisation of access to medicines in trade‐related intellectual property policymaking in Thailand
Bibliographic record
Abstract
Abstract Thailand is facing ongoing trade‐related challenges that threaten access to an affordable and sustainable supply of medicines. Despite Thailand's history of balancing trade pressures and public health priorities, little is known about the factors that enable or constrain a focus on access to medicines in trade‐related intellectual property (IP) decision making. Using document analysis and qualitative interviews, and drawing on Kingdon's Multiple Streams Framework, this qualitative study examines the factors that have enabled or constrained Thailand from focusing on access to medicines in three case studies of trade‐related IP policy: Thailand's patent law and its amendments; its issuance of compulsory licences; and its decision‐making about TRIPS‐plus trade agreements including potential membership of the Comprehensive and Progressive Agreement for Trans‐Pacific Partnership. The degree to which access to medicines has been prioritised in Thailand's trade‐related IP policymaking has varied across different types of policymaking and over time. Integral to its successes has been the involvement of the Ministry of Health and sustained advocacy by access to medicines coalitions which exert political pressure, generate evidence, and provide technical assistance to support evidence‐based policy reform. In addition, Thailand's compulsory licencing was made possible by a policy entrepreneur with the motivation and authority to implement policy change. Constraints to Thailand's focus on access to medicines have included its trade dependence on the United States (US), ongoing US trade pressure to implement TRIPS‐plus measures, and intense lobbying from Pharmaceutical Research and Manufacturers of America, the organisation representing US‐based major pharmaceutical companies, to increase IP protection for pharmaceuticals in Thailand. Through the use of Kingdon's framework, this study's focus on three different types of trade‐related IP policymaking has provided a detailed picture of the factors that have influenced the prioritisation of access to medicines and how these have played out in Thailand. Thailand's mixed history with regard to the prioritisation of access to medicines could provide lessons for other low‐ and middle‐income countries facing similar challenges to access to medicines by ensuring that the conditions are right in each of the three streams for windows of opportunity to emerge.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".