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Enregistrement W3046265197 · doi:10.1108/ijhg-01-2020-0004

Here's to sound action on global hearing health through public health approaches

2020· article· en· W3046265197 sur OpenAlexaffabout
Farah M. Shroff, David Jung

Notice bibliographique

RevueInternational Journal of Health Governance · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueNoise Effects and Management
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésPublic healthRecreationHearing lossOccupational safety and healthNoise-induced hearing lossEnvironmental healthMedicinePublic relationsPolitical scienceNoise exposureAudiologyNursingPathology

Résumé

récupéré en direct d'OpenAlex

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,753
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,426
Tête enseignante GPT0,499
Écart entre enseignants0,073 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2020
Routes d'admission2
Résumé présentoui

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