Effectiveness of interventions promoting hearing health or preventing music-induced hearing loss and/or other auditory symptoms related to musical practice: A Systematic review
Bibliographic record
Abstract
This research discusses the effectiveness of educational programs and preventive interventions aimed at promoting hearing health among musicians. The World Health Organization (WHO) highlights the risk of hearing loss due to noise exposure, including among musicians, where the prevalence of music-induced hearing loss (MIHL) can be significant. We emphasize the need for multidisciplinary approaches, integrating fields such as Music and Audiology, to mitigate these risks. The systematic review, conducted in line with the PRISMA 2020 protocol, focused on studies that evaluated the impact of hearing health interventions on musicians. The review identified four relevant studies, mostly from the United States and Australia, published between 2014 and 2022. These studies primarily focused on the use of hearing protection devices and educational programs to prevent hearing loss. The interventions showed effectiveness in increasing knowledge about hearing protection, changing harmful behaviors, and adopting safe practices. We concluded that while existing studies are limited in number and geographic scope, they demonstrate the efficacy of these programs. More research, especially targeting university music students, are important to develop more comprehensive and effective hearing health interventions tailored to the unique needs of musicians.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".