The impacts on the economy, health, and environment resulting from tobacco cultivation: A cross-sectional survey of tobacco farmer perspectives in Thailand
Bibliographic record
Abstract
INTRODUCTION: Tobacco cultivation is associated with financial instability, health risks, and environmental degradation. While Thailand has made progress in tobacco control, challenges remain in supporting farmers with sustainable alternatives. This study examined the perceived economic, health, and environmental impacts of tobacco cultivation among Thai tobacco farmers. METHODS: A cross-sectional survey was conducted from October 2021 to January 2022 in Chiang Mai, Phrae, and Sukhothai, the major tobacco-growing provinces in Thailand. A total of 1505 tobacco farmers completed self-administered questionnaires. The instrument measured perceived impacts on a 3-point Likert scale (low to high). Frequencies and proportions for descriptive statistics are reported along with adjusted odds ratios and 95% confidence intervals for logistic regression models. RESULTS: Economic impacts were most frequently reported (43.7%), particularly increased debt (47.6%) and income loss (43.5%). Health impacts (31.6%) included symptoms of Green Tobacco Sickness (47.2%) and reduced work capacity (29.9%). Environmental concerns (14.4%) included pesticide contamination (10.8%) and degradation of soil and water resources (10.6%). Higher economic impact was associated with cultivating Virginia tobacco (AOR=6.51; 95% CI: 4.90-8.63), higher level of education (AOR=1.39; 95% CI: 1.01-1.92), contract farming (AOR=1.27; 95% CI: 0.99-1.63), and farming experience (AOR=1.00; 95% CI: 0.99-1.01). Health impact was associated with age (AOR=1.04; 95% CI: 1.03-1.05), land rental (AOR=0.75; 95% CI: 0.58-0.98), female gender (AOR=0.74; 95% CI: 0.58-0.94), and Virginia cultivation (AOR=0.32; 95% CI: 0.23-0.44). Environmental impact was linked to labor hiring (AOR=2.68; 95% CI: 1.41-5.07) and land rental (AOR=0.56; 95% CI: 0.39-0.79). CONCLUSIONS: Thai tobacco farmers face significant economic, health, and environmental burdens. Policy interventions should promote sustainable alternatives to mitigate these impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".