CABI’s Global Health database – Does it have a role to play in informing public health policy?
Bibliographic record
Abstract
CABI's Global Health database has a significant role to play in informing public health policy helping tackle current and emerging health problems across the developing world.Systematic reviews, which are increasing in scope and number, are an excellent tool for evidence-based public health and have a growing influence on policy.However, for them to be meaningful they need to draw upon trustworthy data.Through interviews with researchers and through a literature review we found that CABI's Global Health database is able to offer significant support to systematic reviews, since it covers both print and electronic journals and offers unique content not available elsewhere.The Global Health database, through its use in specific systematic reviews, has contributed to IHME's (Institute for Health Metrics and Evaluation) Global Burden of Disease 2013 report and to a number of WHO clinical guidelines.Its use has informed the funding agenda of international donors, such as DFID and the Wellcome Trust, who are addressing research and practice in humanitarian crisis settings.The database has also been used in developing the research and planning agenda on trachoma for the charity Sightsavers.Moreover, we found that Global Health has a vital role to play in both disseminating locally conducted LMIC research within Africa and globally, by offering coverage of (local) papers in Africa and LMICs, and in nurturing collaboration between groups of researchers/projects.
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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 | 0.061 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.035 | 0.058 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.033 |
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".