Field Attenuation of Foam Earplugs
Bibliographic record
Abstract
BACKGROUND: Hearing protection devices (HPDs) are often used in the workplace to prevent hearing damage caused by noise. However, a factor that can lead to hearing loss in the workplace is improper HPD fitting, and the previous literature has shown that instructing workers on how to properly insert their HPDs can make a significant difference in the degree of attenuation. METHODS: Two studies were completed on a total of 33 Hydro One workers. A FitCheck Solo field attenuation estimation system was used to measure the personal attenuation rating (PAR) before and after providing one-on-one fitting instructions. In addition, external ear canal diameters were measured, and a questionnaire with items related to frequency of use, confidence, and discomfort was administered. RESULTS: Training led to an improvement in HPD attenuation, particularly for participants with poorer PARs before training. The questionnaire results indicated that much HPD discomfort is caused by heat, humidity, and communication difficulties. External ear canal asymmetry did not appear to significantly influence the measured PAR. CONCLUSION: In accordance with the previous literature, our studies suggest that one-on-one instruction is an effective training method for HPD use. Addressing discomfort issues from heat, humidity, and communication issues could help to improve the use of HPDs in the workplace. Further research into the effects of canal asymmetry on the PAR is needed.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".