Risk Management of Lead and Arsenic Poisoning in Children through Public Participation in Communities near Abandoned Tin Mine, Southern Thailand
Bibliographic record
Abstract
Tamtalu Subdistrict, Bannangsta District in Yala Province, southern Thailand was the site of an abandoned tin mine, and was contaminated by lead and arsenic from the mine tailings. As children are a high risk group from these highly toxic contaminants, this study aimed to identify approaches to reduce children’s exposure in the area. The study was conducted through participatory action research (PAR) to empower the community strengthen sustainable risk management. The participants were local public health officials, public health volunteers, parents of local children and local community leaders. The participants were engaged in activities relating to risk communication, training on exposure prevention, planning of risk management and implementation of the plan. The children’s risks were communicated to the villagers during community meetings. Trainings on how to prevent As and Pb exposure were provided, and preventive strategies planned and implemented. After a six-month period of the intervention, levels of As and Pb in the hair of local children’s decreased significantly (p< 0.01 and p< 0.05, respectively). The parents’ knowledge of how to prevent children from As and Pb exposure increased significantly (p<0.01). The results indicated that PAR can be used to mitigate problems of chronic environmental exposure and poisoning. In addition, the Ottawa Chatter concept can be applied for long-term management. Economic status and effective risk reduction programmes are key determinants for successful implementation of local community-based risk management plans.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".