Are dental hygienists at risk for noise-induced hearing loss? A literature review.
Bibliographic record
Abstract
Objective: The aim of this review is to explore dental hygienists' risk for noise-induced hearing loss (NIHL) and to describe the current hearing protection options. Methods: A literature search was undertaken using the following databases: PubMed, CINAHL, Cochrane Libraries, and Google Scholar. The returns were screened using inclusion and exclusion criteria and the remaining studies were critically appraised. Results and Discussion: Seventeen articles assessed noise levels and NIHL risk in dental settings; and 11 articles examined hearing protection devices. The literature revealed that oral health practitioners were exposed to excessive noise limits (85 dBA) in an 8-hour workday, therefore increasing the risk of NIHL. Oral health professionals need to be aware of this risk and the preventive measures they can take to reduce the potential for hearing loss. Effective preventive measures may include hearing protective devices (HPDs), educational programs, insulated noise-absorbing materials, and regular monitoring of noise exposure. Conclusion: Dental hygienists may be at risk for permanent or temporary hearing loss in their work environment. Permanent hearing loss from the use of ultrasonic scalers appears to be minimal. To prevent hearing loss, active (electronic) HPDs are recommended as they allow practitioners to protect their hearing and communicate with clients.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".