Here's to sound action on global hearing health through public health approaches
Notice bibliographique
Résumé
Purpose A global pandemic, non-occupational noise-induced hearing loss (NIHL) is a completely preventable public health problem, which receives limited air time. This study has dual purposes: to contribute to scholarly literature that puts non-occupational NIHL on the global priority map and to effect change in the City of Vancouver's policies toward noise. Design/methodology/approach Experts in public health and hearing health were contacted in addition to a scoping literature search on PubMed. Information pertaining to both developed and developing countries was obtained, and comparison was made to Canada where possible. The authors met with elected officials at the City of Vancouver to inform them of the win–win aspects of policies that promoted better hearing. Findings Non-occupational NIHL is an underappreciated issue in Canada and many other countries, as seen by the lack of epidemiological data and public health initiatives. Other countries, such as Australia, have more robust research and public health programs, but most of the world lags behind. Better hearing health is possible through targeted campaigns addressing root causes of non-occupational, recreational noise – positive associations with loud noise. By redefining social norms so that soft to moderate sounds are associated with positive values and loud sounds are negatively attributed, the societies will prevent leisure NIHL. The authors recommend widespread national all-age campaigns that benefit from successful public health campaigns of the past, such as smoking cessation, safety belts and others. Soft Sounds are Healthy (SSH) is a suggested name for a campaign that would take many years, ample resources and sophisticated understanding of behavior change to be effective. Research limitations/implications A gap exists in the collection of non-occupational NIHL data. Creating indicators and regularly collecting data is a high priority for most nations. Beyond data collection, prevention of non-occupational NIHL ought to be a high priority. Studies in each region would propel understanding, partly to discern the cultural factors that would predispose the general population to change favorable attitudes toward loud sounds to associations of moderate sounds with positivity. Evaluations of these campaigns would then follow. Practical implications Everyday life for many people around the world, particularly in cities, is loud. Traffic, construction, loudspeakers, music and other loud sounds abound. Many people have adapted to these loud soundscapes, and others suffer from the lack of peace and quiet. Changing cultural attitudes toward loud sound will improve human and animal health, lessen the burden on healthcare systems and positively impact the economy. Social implications Industries that create loud technologies and machinery ought to be required to find ways to soften noise. Regulatory mechanisms that are enforced by law and fines ought to be in place. When governments take up the banner of hearing health, they will help to set a new tone toward loud sounds as undesirable, and this will partially address the root causes of the problem of non-occupational NIHL. Originality/value Very little public health literature addresses NIHL. It is a relatively ignored health problem. This project aims to spurn public health campaigns, offering our own infographic with a possible title of Soft Sounds are Healthy (SSH) or Soft Sounds are Sexy (SSS). The study also aimed to influence city officials in the authors’ home, Vancouver, and they were able to do this.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».