A Protocol for a Scoping Study of Economic and Data System Considerations for Climate Change and Pandemic Preparedness in Africa
Bibliographic record
Abstract
Abstract Background Africa’s changing climate heightens risk of disease outbreaks with impacts on vulnerable demographics already encumbered by health and socioeconomic burdens. Health preparedness policies to address disease outbreaks rely on integrated information systems and value-for-cost analysis to facilitate sound decision-making during a public health emergency. This rapid scoping review protocol describes the a priori objectives and methods to conduct a synthesis of the evidence on economic evaluation and data system capacity at the intersection of pandemic preparedness and climate change. Methods A search of six bibliographic databases will be conducted by a library information professional spanning the period 2010 – 2023, focused on literature published about pandemic preparedness in Africa in the context of climate change. Studies will be screened in a three-stage process by independent reviewers using Covidence software, with a proportion of excluded articles crosschecked by a reviewer not involved in screening. All articles included in the final study set will need to have a positive response to at least three out of four a priori screening questions. Data extraction will follow established frameworks for pandemic preparedness, a list of 14 target climate-sensitive infectious diseases with pandemic potential declared as Public Health Emergencies of International Concern and listed on the WHO’s R&D Blueprint Pathogens, and economic evaluation or data systems domains. Evidence synthesis will include article bibliometric analysis as well as thematic topic categorisation. Gap analysis will be conducted through topic mapping. Discussion This protocol lists the methods and analysis that will be followed to survey the literature on the linkages between climate change and pandemic preparedness relating to economic evaluation and data systems structures and needs.
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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 | 0.188 | 0.226 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.184 | 0.042 |
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".