Does health-related poverty publication landscape reflect global needs in the light of the current poverty rebound?
Bibliographic record
Abstract
BACKGROUND: After nearly a quarter-century of declining poverty, the numbers are rising again significantly. This is due not only to effects of climate change but also to the COVID-19 pandemics and armed conflict. Combined with the enormous health impacts, that will cause misery and health care costs worldwide. Therefore, this study provides background information on the global research landscape on poverty and health to help researchers, stakeholders, and policymakers determine the best way to address this threat. RESULTS: The USA is the key player, dealing mainly with domestic issues. European countries are also involved but tend to be more internationally oriented. Developing countries are underrepresented, with Nigeria standing out. A positive correlation was found between publication numbers and economic strength, while the relationship between article numbers and multidimensional poverty was negatively correlated. CONCLUSIONS: These findings highlight the need for advanced networking and the benefits of cross-disciplinary research to mitigate the coming impacts.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".