Effects of uranium mining on health: a case study of Jadugoda of Jharkhand, India
Bibliographic record
Abstract
While the measurement of radiological risk and its environmental and ecological impacts has been extensively studied, the effects on human health particularly among populations living near mining areas have received comparatively less attention. In this context, the present study investigates the health impacts of uranium mining on residents living in close proximity to the Jadugoda uranium mining region in India. Drawing on in-depth interviews with 37 respondents and 4 health workers from three villages adjacent to the mining site, the study reveals a high prevalence of chronic health conditions. These include respiratory issues (such as bronchitis, chest pain, and chronic coughing), skin diseases, various forms of cancer, neuropsychiatric disorders, congenital disabilities (e.g., birth defects, developmental delays, and physical deformities), and reproductive health problems (such as irregular menstruation, excessive bleeding, urinary infections, and miscarriage). In addition to these physical ailments, participants reported severe psychological distress, including anxiety, fear, and suicidal ideation. According to community members, unprotected tailing ponds where mining waste is improperly stored are perceived as a primary source of radiological exposure. In light of these findings, the study calls for urgent public health interventions and further research into the long-term health consequences of uranium mining in affected regions. We recommend several key interventions including strengthening local healthcare systems, enforcing safe mining and waste management regulations, and regularly monitoring radiation levels. Financial support for affected individuals especially cancer patients and collaboration among public health agencies, local authorities, and civil society are essential for an effective response.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".