Perspectives of different stakeholders on data use and management in public health emergencies in sub-Saharan Africa: a meeting report
Bibliographic record
Abstract
<ns4:p>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.</ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".