Measurement of Occupational Sound Exposure from Communication Headsets
Bibliographic record
Abstract
Increased use of communication headsets found in various workplaces raises concerns regarding exposure to potentially hazardous noise levels. Current national and international standards specify a wide range of simple and specialized methods for the measurement of sound exposure under communication headsets. However, to date, quantitative data comparing the degree of agreement between the different measurement methods or their relative performance are lacking, and it is not known if occupational health and safety (OHS) or hearing loss prevention (HLP) stakeholders have the necessary training and equipment to integrate them in their daily practice. A three-step study addressing several knowledge gaps on this topic is presented in this thesis. First, a questionnaire survey distributed to OHS and HLP stakeholders has revealed that knowledge of specialized measurement techniques and access to the necessary equipment varies significantly depending on the training of the different professionals. There is therefore reason to specify several methods in measurement standards to meet the specific needs and expertise of the different stakeholders involved. Second, a series of experiments conducted with single and multiple expert participants indicated that the Type 1 artificial ear is not suited for sound exposure measurement with communication headsets, while Type 2 and Type 3.3 artificial ears are in good agreement with the acoustic manikin technique specified in the International standard ISO 11904-2. Finally, laboratory experiments were conducted to test the indirect calculation method proposed in the Canadian standard CSA Z107.56. Results revealed that the calculation method is suitable to identify possible situations of exposure over the regulatory limit (e.g. 85 dBA), but refinements are proposed to improve measurement accuracy. Overall, this thesis provides new knowledge to guide selection of the most suitable methods for the assessment of communication headset exposure taking into account expertise, access to equipment, and field logistic constraints. Results also have direct implications for future revisions of existing measurement standards. Finally, this work could be the basis for detailed guidelines on noise exposure measurements under communication headsets to better inform OHS and HLP professionals and ultimately prevent occupational noise-induced hearing loss.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".