Towards a better integration of social sciences in arbovirus research and decision-making: an experience from scientific collaboration between Cuban and Quebec institutions
Bibliographic record
Abstract
In 2017, the Institute of Tropical Medicine Pedro Kourí, University of Montreal Public Health Research Institute, and McGill University joined efforts to provide scenarios for scientific exchange and knowledge dissemination about the social science contribution on arboviral research. This commentary describes the scientific collaboration between Cuban and Canadian (Quebec) institutions, illustrating the need and opportunities to facilitate research and effective decision-making processes for arboviral prevention and control, going beyond traditional biomedical aspects. We organized a set of scientific activities within three international events conducted in Cuba between 2017 and 2018. Given the collaborating institutions' expertise and the knowledge gaps in arboviral research, we selected three main thematic areas: social determinants and equity, community-based interventions and use of evidence for decision-making. The partnership shows that interdisciplinary collaboration and the use and integration of quantitative and qualitative methods from the social sciences is essential to face the current challenges in arbovirus research.
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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 | 0.048 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.021 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".