Perspectives of different stakeholders on data use and management in public health emergencies in sub-Saharan Africa: a meeting report
Bibliographic record
Abstract
During public health emergencies (PHEs), data are collected and generated from a variety of activities and sources, including but not limited to national public health programs, research and community-based activities. It is critical that these data are rapidly shared in order to facilitate the public health response, epidemic preparedness, as well research during and after the epidemic. Nonetheless, collecting and sharing data during PHEs can be challenging, especially where there are limited resources for public health and research-related activities during a PHE. In a symposium that brought together different stakeholders that were involved in the 2013-2016 Ebola outbreaks in West Africa, meeting attendees shared their perspectives on the values and management of data during PHEs in sub-Saharan Africa. Key factors that could inform and facilitate data management during PHEs in sub-Saharan Africa were discussed, including using data to inform policy decisions and healthcare; a coordinated data collection and management scheme; identifying incentives for data sharing; and equitable data governance mechanism that emphasise principles of reciprocity, transparency and accountability rather that trust between stakeholders or collaborators. Empirical studies are required to explore how these principles could inform best practices for data management and governance during PHE in sub-Saharan Africa.
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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 | 0.127 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".