Comparison of occupational noise legislation in the Americas: An overview and analysis
Bibliographic record
Abstract
The workplace contributes significantly to the total dose of daily noise to which a person is subjected. Therefore, millions of people around the world are exposed to potentially dangerous noise levels and consequently, there is an urgent, global need for legislation to adequately protect the auditory health of workers. Occupational noise legislation has been adopted in many of the countries with different degrees of comprehensiveness and varying levels of sophistication. This paper presents a global view of current legislation on occupational noise in the 22 countries that make up the Americas, that is, Latin America, Canada, and the United States. Upon analysis of the legislation, there are notable differences among countries in the defined values for permissible exposure limit (PEL) and exchange rate. Of the countries that have regulations, the majority (81%) use a PEL of 85 dBA. A PEL of 85 dBA and the 3-dB exchange rate are currently used by 32% of the nations in the Americas. Most nations limit impulsive noise exposure to a peak unweighted sound pressure level of 140 dB (or dBC), while a few use slightly lower limits. However, 27% of the countries in the region still have not established regulations with respect to permissible noise levels and exchange rates. This fact is leaving millions of workers in the Americas unprotected against occupational noise. Provide an overview and analysis of the current legislation on occupational noise in the 22 countries that make up the Americas. The information on legislation, regulations, and standards discussed in this paper were obtained directly from official government sources in each country, the International Labour Organization database, or through various colleagues in each country. (1) There are notable differences among countries in the defined values for PEL and exchange rate. (2) Of the countries that have regulations, the majority (81%) use a PEL of 85 dBA. A PEL of 85 dBA and the 3-dB exchange rate are currently used by 32% of the nations in the Americas. (3) Most nations limit impulsive noise exposure to a peak unweighted sound pressure level of 140 dB (or dBC), while a few use slightly lower limits. (4) 27% of the countries in the region still have not established regulations with respect to permissible noise levels and exchange rates. (5) Millions of workers in the Americas are unprotected against occupational noise.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".