Global Health Resilience Building: A Population Health Equity Approach to the Agro-Pastoralist Community in Drought Affected Ethiopia
Bibliographic record
Abstract
Devastating droughts have been ravaging Africa for decades. Ethiopia is the worst-hit country as it’s economy predominantly depends on rain-fed farming and livestock. The agriculture sector contributes nearly 85% of the country’s livelihoods. The drought has threatened agro-economy and health of over 15 million agro-pastoralist population who herd the largest livestock in Africa. While foreign aids could be a temporary relief, Sendai framework for Disaster Risk Reduction prioritizes proactive response to promote resilience for the affected people. Applying a population health equity approach can serve the purpose by exploring the determinants, their impacts on the differential health outcomes for the population sub-groups and addressing the health inequity to improve the overall health of the population. Our objective was to identify the critical population health outcomes, underlying determinants, and the leverage points for actions that could guide effective policies interventions for building health resilience for the vulnerable agro-pastoralist population in Ethiopia. To summarize and assess the quality of data, we utilized the PRISMA and Hamilton tools, respectively. We used disaster vulnerability and the WHO's socioeconomic determinants of health and health equity frameworks to synthesize evidence Our analyses showed that to identify policy and leverage areas for effective population health interventions, researchers looked at socioeconomic, political, and cultural backgrounds. Health issues are diverse that revolve around the major determinants of health such as food security, infrastructure, health systems, disaster preparedness, household productivity or income, livestock dependence and access to the market economy. The socioeconomic, political, and cultural environments - all have an impact on these factors. Despite extreme vulnerability and health inequalities, modern public health field practices have yielded some potentials for policy solutions. To achieve the greatest impact on health resilience, the recommended interventions might be guided by an interdisciplinary population health approach. Evidence from Africa's worst drought niche can help address comparable drought-related health challenges in other parts of the continent.
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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 | 0.048 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".