Reducing agate dust exposure in Khambhat, India: Protective practices, barriers, and opportunities
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".