Advancing in the institutionalization of Evidence-Informed Policy in Brazil
Bibliographic record
Abstract
Abstract EVIPNet Brazil was launched in 2007, coordinated by the Ministry of Health. Its expansion has promoted the institutionalization of a national knowledge translation platform, and has also been active at the local level. Local Health Evidence Labs were implemented to institucionalize working groups linked to EVIPNet. In this multiple case study, we discuss the advances in the institutionalization of Evidence-Informed Policy-making in Brazil, based on a mapping of the Health Evidence Labs' institutional capacity to acquire, evaluate, adapt and apply evidence and analyzed organizational arrangements and implementation barriers. The coordinators of 15 responded to a self-assessment questionnaire from the Canadian Health Services Research Foundation. The main products reported are evidence briefs and deliberative dialogues focused on regional problems, training activities and different kinds of rapid responses. Health Evidence Labs have the resources to acquire and evaluate research evidence, but have a low capacity to adapt and apply evidence, indicating that they do not have all the factors (skills, structures, processes, and organizational culture) to promote and use research results in decision-making. The lower capacity to 'adapt' and 'apply' evidence may be related to low development of exchange process with external experts. Governance is joined between managers of the departments with technical bodies; the service provision occurs to attend internal and external demands; financing is public or utilizing public research calls. The implementation barriers described are the staff turnover, inadequate funding arragements for develop knowledge translation products, paid access to evidence databases, lack of a repository for their production, and lack of monitoring and evaluation. Brazil has improved in the institutionalization of evidence-informed policy but still some important barriers to achieve a complete national knowledge translation platform. Key messages Brazil has institutionalized evidence-informed policy from the structuring of EVIPNet Brazil Network. To achieve the network sustainability, it is vital to consider the alignment of organizational elements with an emphasis on governance, financing, and its monitoring.
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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 | 0.069 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".