Reducing agate dust exposure in Khambhat, India: Protective practices, barriers, and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: Agate workers in Khambhat, India and their community members are exposed to high levels of silica dust and related diseases. Use of effective prevention practices remains low, prompting the need for effective interventions which increase the uptake of and investment in prevention practices. We sought: (a) to describe knowledge, self-efficacy, and practices among a population of workers, their family members, and neighbors involved in or located close to agate processing; and (b) to explore which factors are related to use of prevention practices and willingness to invest in new dust control technologies. METHODS: A community survey was conducted to measure demographics, occupation and financial factors, knowledge, prevention practices, barriers, risk perceptions, and efficacy beliefs. Descriptive statistics were used and, among agate workers, hierarchical logistic regression explored predictors of prevention practice use and willingness to invest. RESULTS: Among 1120 respondents, approximately 44%, 35%, and 8% of workers, family members, and neighbors used prevention practices, respectively. Knowledge and risk perceptions were generally high, where efficacy beliefs were low. Workers who had high levels of education, worked at home, and had high efficacy beliefs were more likely to report using prevention practices and being willing to invest. Barriers to prevention practice use included financial barriers, and beliefs that prevention is ineffective and health is not at risk. CONCLUSIONS: Interventions and future research should be designed to engage the community to improve preventive behavior, and implement affordable and effective dust control interventions in the agate industry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it