Is Agricultural Intensification a Growing Health Concern? Perceptions from Waste Management Stakeholders in Vietnam
Bibliographic record
Abstract
This article characterizes the health risk perceptions toward excreta and wastewater management practices among waste management stakeholders in Vietnam and explores the implications of such perceptions on hygiene behaviors and preventative actions. Key informant interviews (n = 19; 12 women and 7 men) were conducted with farmers, community leaders, researchers, and government representatives in Hanoi and Ha Nam Province. Interviews were audio-recorded with permission, transcribed, and analyzed using a constant comparative method and qualitative thematic analysis. Researchers and government representatives perceived that the lack of knowledge of safe waste management practices among farmers was responsible for the use of “outdated” and often “unsafe” waste management practices. However, many farmers were aware of the health risks and safe hygienic practices but felt that safety measures were impractical and viewed susceptibility to diseases as low risk. Farmers also identified unfavorable climate and working conditions, limited financial capacity, and limited farm space as barriers to adopting safe management practices. At the broader level, inadequate communication between ministries often led to the creation of inconsistent waste management regulations. These barriers create constraints on efforts to improve sustainable waste management practices. Promoting collaboration between sectors, encouraging farmer-to-farmer knowledge sharing, and designing and implementing risk communication strategies that account for risk perceptions of stakeholders are recommended.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".