Impact of research on the Support Program for the Institutional Development of the SUS (Unified Health System)
Bibliographic record
Abstract
ABSTRACT Measuring the impact of investment in research can contribute to a better understanding of the achievement of results in health systems and to guide the management of resources for priority areas. This study sought to evaluate the impact of ‘advances in knowledge’ produced by research on health funded by the Support Program for the Institutional Development of the Unified Health System (Proadi-SUS) between 2009 and 2014 in Brazil. It is an evaluative investigation based on the institutional records of project monitoring and accountability reports presented by the research institutions. The impact analysis used some of the indicators proposed by the adapted health research evaluation model of the Canadian Academy of Health Sciences (CAHS). The global investments were of R$ 66.49 million in 46 investigations and identified 12 subject areas, distributed in five types of studies. The main area was cardiology. The impact analysis identified the results of 28 projects (60.8%). It was possible to observe potential advances in knowledge in the field of chronic non-infectious diseases. The transfer of knowledge generated by these surveys and the impact of investing on informed decision-making and health sector benefits have not been measured and remain challenges for an effective evaluation of the program. Studies that evaluate the use of evidence produced in clinical practice and management can help understand the impact of research funded by the Proadi-SUS in other dimensions.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".