MétaCan
Menu
Back to cohort
Record W2990873785 · doi:10.1186/s12992-019-0518-2

Analyzing the impact of trade and investment agreements on pharmaceutical policy: provisions, pathways and potential impacts

2019· article· en· W2990873785 on OpenAlexafffund
Deborah Gleeson, Joel Lexchin, Ronald Labonté, Belinda Townsend, Marc‐André Gagnon, Jillian Clare Köhler, Lisa Forman, Kenneth C. Shadlen

Bibliographic record

VenueGlobalization and Health · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of OttawaPublic Health OntarioUniversity of TorontoCarleton UniversityYork University
FundersUniversity of TorontoUniversitetet i OsloLa Trobe UniversityUniversity of Ottawa
KeywordsSocial policyHealth services researchInvestment (military)Health policyPublic healthEconomicsHealthcare policyBusinessHealth economicsPublic economicsInternational tradeInternational economicsPolitical scienceHealth care reformEconomic growthHealth careLawMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Trade and investment agreements negotiated after the World Trade Organization's Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) have included increasingly elevated protection of intellectual property rights along with an expanding array of rules impacting many aspects of pharmaceutical policy. Despite the large body of literature on intellectual property and access to affordable medicines, the ways in which other provisions in trade agreements can affect pharmaceutical policy and, in turn, access to medicines have been little studied. There is a need for an analytical framework covering the full range of provisions, pathways, and potential impacts, on which to base future health and human rights impact assessment and research. A framework exploring the ways in which trade and investment agreements may affect pharmaceutical policy was developed, based on an analysis of four recently negotiated regional trade agreements. First a set of core pharmaceutical policy objectives based on international consensus was identified. A systematic comparative analysis of the publicly available legal texts of the four agreements was undertaken, and the potential impacts of the provisions in these agreements on the core pharmaceutical policy objectives were traced through an analysis of possible pathways. RESULTS: An analytical framework is presented, linking ten types of provisions in the four trade agreements to potential impacts on four core pharmaceutical policy objectives (access and affordability; safety, efficacy, and quality; rational use of medicines; and local production capacity and health security) via various pathways. CONCLUSIONS: The analytical framework highlights provisions in trade and investment agreements that need to be examined, pathways that should be explored, and potential impacts that should be taken into consideration with respect to pharmaceutical policy. This may serve as a useful checklist or template for health and human rights impact assessments and research on the implications of trade agreements for pharmaceuticals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.009
Science and technology studies0.0030.007
Scholarly communication0.0100.008
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.111
GPT teacher head0.333
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueGlobalization and HealthSame topicIntellectual Property and PatentsFrench-language works237,207