A furnished house without walls: Examining the work and health support systems of self-employed workers in Ontario, Canada
Bibliographic record
Abstract
Introduction: Many small and developing countries still allow asbestos use due to their vulnerability to the economic pressure from powerful asbestos exporting countries.The lack of awareness of asbestos hazards is a potential reason for the continued use of asbestos in such vulnerable countries.St. Kitts and Nevis is one such country where asbestos has not been banned.This study aimed to investigate the knowledge, attitude, and awareness of asbestos among the residents of St. Kitts and Nevis.Materials and Methods: We used an online questionnaire to collect participants' knowledge about asbestos-containing products, awareness of asbestos exposure and health risks, and attitudes about preventive measures to asbestos exposure among 1,009 residents age 18 years in St. Kitts and Nevis.Results: Over 50% of participants ever heard the term "asbestos" (71%) and knew what asbestos is (56%).Only <30% of participants knew asbestos-related diseases (26%) and their latency periods (20%).As for attitudes, >70% of participants thought the general public should be concerned about asbestos exposure in their daily life, and it is the government's duty to prevent such exposure.54% of participants thought that a total ban is the best option to prevent asbestos exposure in the country.Conclusions: Residents of St. Kitts and Nevis were aware of asbestos but had low awareness of health effects related to asbestos exposure.The government of St. Kitts and Nevis should consider implementing educational programs to increase the knowledge and awareness of asbestos-related diseases and taking actions towards a total asbestos ban country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".