Governance and pharmacovigilance in Brazil: a scoping review
Bibliographic record
Abstract
BACKGROUND: This scoping review investigates the relationship between governance, pharmacovigilance, and Agencia Nacional de Vigilancia Sanitaria (ANVISA) in Brazil, which has authority over Brazil's national pharmaceutical policy, drug registration and coordination of the national pharmacovigilance system. The purpose is to investigate opportunities for effective pharmacovigilance. METHODS: Sixty-three terms pertaining to pharmacovigilance in Brazil and ANVISA, global institutions, pharmaceutical industry, and civil society were searched in thirteen relevant databases on November 17-18, 2013. Using a pharmacogovernance framework we analyzed ANVISA's pharmacogovernance: the manner in which governing structures, policy instruments, and institutional authority are managed to promote societal interests for patient safety due to medication use. The integration of transnational policy ideas for regulatory governance into pharmacogovernance in Brazil was also investigated. RESULTS: Brazil's policy, laws, and regulations support ANVISA's authority to ensure access to safe medicines and health products however ANVISA's broad mandate and gaps in pharmacogovernance account for regional disparities in monitoring and assessing drug safety. Gaps in pharmacogovernance include: equity and inclusiveness; stakeholder coordination; effectiveness and efficiency; responsiveness; and intelligence and information. CONCLUSIONS: Pharmacogovernance that addresses 1) regional resource disparities, 2) federal and state lack of coordination of pharmacovigilance regulations, 3) asymmetric representation in the pharmaceutical regulatory agenda and which 4) disaggregates regulatory authority over health and commercial sectors would strengthen pharmacovigilance in Brazil.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".