Bibliographic record
Abstract
BACKGROUND: Health and safety legislation is designed to protect workers from hazards, including excessive noise. However, some workers are not required to use hearing protection when exposed to loud noise and may be vulnerable to adverse outcomes, including hearing difficulties and tinnitus. DATA AND METHODS: Data for 19- to 79-year-olds (n=6,571) were collected from 2012 through 2015 as part of the Canadian Health Measures Survey. People exposed to loud workplace noise were defined as those who had to raise their voices to communicate at arm's length. Vulnerable workers were defined as those who were not required to use hearing protection when working in noisy environments and who only used hearing protection sometimes, rarely or never. RESULTS: An estimated 11 million Canadians (43%) have worked in noisy environments, and over 6 million of them (56%) were classified as vulnerable to workplace noise. Although the percentage of vulnerable women (72%) was greater than that of men (48%), men outnumbered women in these circumstances at 3.7 million, compared with 2.4 million. The self-employed were more likely than employees to be vulnerable, as were those in white-collar versus blue-collar occupations. Vulnerable workers were more likely to report hearing difficulties and tinnitus than those who had never worked in a noisy environment. DISCUSSION: A large percentage of workers exposed to noisy workplaces were vulnerable because hearing protection was neither required nor routinely used. Further work is required to assess whether this reflects gaps in health and safety legislation or its implementation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".