The Recent 2023 Earthquake in Nepal: A Global Health Perspective
Bibliographic record
Abstract
As a seismic hotspot, Nepal has endured many catastrophic earthquakes, including the 2023 Jajarkot quake. These quakes worsen the existing fragilities, resulting in difficulties in accessing healthcare, outbreaks of infectious diseases, mental health problems, and nutritional shortfalls. The article examines the complex web of health consequences, such as infectious and non-infectious diseases and malnutrition, highlighting the need for a global health lens in tackling these issues. It also reveals the long-term health effects, such as mental health disorders and increased disease susceptibility, that emerge after the quake and the importance of enhancing coordination and communication, enforcing building codes, and assisting affected communities in response to the seismic hazards. The article identifies mitigation strategies, community involvement, and international cooperation as key elements in building resilience against future quakes. It discusses the role of climate change in seismic risks and the need for research, innovation, and adaptability in global health interventions, suggesting measures such as strengthening primary healthcare, preventing avoidable health problems through education, and improving supply chains. The article calls for a holistic approach to building resilient health systems, emphasizing community engagement, prevention, and preparedness to protect the health of vulnerable groups in seismic regions.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".