Impact of dengue research funded by the Ministry of Health in Brazil
Bibliographic record
Abstract
ABSTRACT This study assessed the impact of 24 dengue research projects funded by the Department of Science and Technology of the Ministry of Health, in partnership with the National Council for Scientific and Technological Development, in the years 2006, 2008, and 2012, using the dimensions of knowledge advancement, research capacity, informed decision-making, and health impacts as reference from the Impact Evaluation Framework of the Canadian Academy of Health Sciences. Data were collected through document reviews, questionnaires, and interviews with the coordinators of the dengue research projects. A total of 1,107 impacts were identified, with the majority in the dimensions of knowledge advancement (712) and research capacity (314). Within these two dimensions, notable mentions include disseminating results at conferences (390) and publishing scientific articles (166). There was less impact in the dimensions of decision-making (75) and health impacts (7); however, it is essential to highlight the dissemination of research results in the media (43) and impacts on health determinants (5). This study highlighted the diversity of impacts produced by dengue research across the evaluated dimensions, demonstrating the importance of impact evaluation in identifying benefits and justifying investments. Thus, it contributes to strengthening the capacity of the Brazilian research system to address dengue.
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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 | 0.038 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".