Children who say hand dryers ‘hurt my ears’ are correct: A real-world study examining the loudness of automated hand dryers in public places
Bibliographic record
Abstract
INTRODUCTION: Previous research has suggested that hand dryers may operate at dangerously loud levels for adults. No research has explored whether they operate at a safe level for children's hearing. Children's ears are more sensitive to damage from loud sounds than adult ears. Health Canada prohibits the sale of toys with peak loudness greater than 100 dB. This study tested installed dryers in public washrooms to see if they were safe for children's hearing. METHODS: Forty-four hand dryers in public washrooms were each measured for peak sound levels in a standardized fashion, including at children's ear canal heights. Each dryer was measured at 10 different combinations of heights and distances from the wall, and with and without hands in the air stream coming from the hand dryer, for a total of 20 measurements per dryer. RESULTS: Xlerator units performed the loudest, with all being louder than 100 dBA at all measurements whenever hands were in the airstream. Several Dyson Airblade models were also very loud, including the single loudest measurement of 121 dBA. While some other units operated at low sound levels, many units were louder at children's ear heights than at adult ear heights. DISCUSSION: Many dryers operated much louder than their manufacturers claimed, usually greater than 100 dBA (the maximum allowable noise level for products/toys meant for children). CONCLUSION: This study suggests that many hand dryers operate at levels far louder than their manufacturers claim and at levels that are clearly dangerous to children's hearing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".