Survey of Argentine Health Researchers on the Use of Evidence in Policymaking
Bibliographic record
Abstract
OBJECTIVE: In this study, Argentine health researchers were surveyed regarding their perceptions of facilitators and barriers to evidence-based policymaking in Argentina, as well as their publication activities, and research environment satisfaction. METHODS: A self-administered online survey was sent to health researchers in Argentina. The survey questions were based on a preceding qualitative study of Argentine health researchers, as well as the scientific literature. RESULTS: Of the 647 researchers that were reached, 226 accessed the survey, for a response rate of 34.9%. Over 80% of researchers surveyed had never been involved in or contributed to decision-making, while over 90% of researchers indicated they would like to be involved in the decision-making process. Decision-maker self-interest was perceived to be the driving factor in the development of health and healthcare policies. Research conducted by a research leader was seen to be the most influential factor in influencing health policy, followed by policy relevance of the research. With respect to their occupational environment, researchers rated highest and most favourably the opportunities available to present, discuss and publish research results and their ability to further their education and training. Argentine researchers surveyed demonstrated a strong interest and willingness to contribute their work and expertise to inform Argentine health policy development. CONCLUSION: Despite Argentina's long scientific tradition, there are relatively few institutionalized linkages between health research results and health policymaking. Based on the results of this study, the disconnect between political decision-making and the health research system, coupled with fewer opportunities for formalized or informal researcher/decision-maker interaction, contribute to the challenges in evidence informing health policymaking in Argentina. Improving personal contact and the building of relationships between researchers and policymakers in Argentina will require taking into account researcher perceptions of policymakers, as highlighted in this study.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".