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Enregistrement W2755174194 · doi:10.22605/rrh3809

A comparative analysis of policies addressing rural oral health in eight English-speaking OECD countries

2017· article· en· W2755174194 sur OpenAlexaboutno aff
LA Crocombe, Lynette R. Goldberg, Erica Bell, Bastian Seidel

Notice bibliographique

RevueRural and Remote Health · 2017
Typearticle
Langueen
DomaineDentistry
ThématiqueDental Health and Care Utilization
Établissements canadiensnon disponible
Organismes subventionnairesAustralian GovernmentAustralian Primary Health Care Research Institute, Australian National UniversityPrimary Health Care Research, Evaluation and Development
Mots-clésHealth policyRural areaGovernment (linguistics)Rural healthEconomic growthPovertyHealth careMedicinePublic healthEnvironmental healthPolitical scienceNursing

Résumé

récupéré en direct d'OpenAlex

INTRODUCTON: Oral health is fundamental to overall health. Poor oral health is largely preventable but unacceptable inequalities exist, particularly for people in rural areas. The issues are complex. Rural populations are characterised by lower rates of health insurance, higher rates of poverty, less water fluoridation, fewer dentists and oral health specialists, and greater distances to access care. These factors inter-relate with educational, attitudinal, and system-level issues. An important area of enquiry is whether and how national oral health policies address causes and solutions for poor rural oral health. The purpose of this study was to examine a series of government policies on oral health to (i) determine the extent to which such policies addressed rural oral health issues, and (ii) identify enabling assumptions in policy language about problems and solutions regarding rural communities. METHODS: Eight current oral health policies were identified from Australia, New Zealand, Canada, the USA, England, Scotland, Northern Ireland, and Wales. Validated content and critical discourse analyses were used to document and explore the concepts in these policy documents, with a particular focus on the frequency with which rural oral health was mentioned, and the enabling assumptions in policy language about rural communities. RESULTS: Seventy-three concepts relating to oral health were identified from the textual analysis of the eight policy documents. The rural concept addressing oral health issues occurred in only 2% of all policies and was notably absent from the oral health policies of countries with substantial rural populations. It occurred most frequently in the policy documents from Australia and Scotland, less so in the policy documents from Canada, Wales, and New Zealand, and not at all in the oral health policies from the US, England, and Northern Ireland. Thus, the oral health needs of rural communities were generally not the focus of, nor included in, the oral health policy documents in this study. When the language of concepts related to rural oral health was examined, the qualitative analysis identified four discourse themes related to both causality and solutions. These ranked discourse themes focused on service models, workforce issues, social determinants of health, and prevention. None of the policies addressed the structural economic determinants of unequal rural oral health, nor did they specifically assert the rights of children in rural communities to equitable oral health care. CONCLUSIONS: This study documented the limited focus on rural oral health that existed in national oral health policies from eight different English-speaking countries. It supports the need for an increased focus on rural oral health issues in oral health policies, particularly as increased oral health is clearly associated with increased general health. It speaks to the critical importance of periodic analysis of the content of oral health policies to ensure that issues of inequality are addressed. Further, it reinforces the need for research findings about effective oral health care to be translated into practice in the development of practical and financially viable policies to make access to oral health care more equitable, particularly for people living in rural and remote areas.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,419
Score d'incertitude au seuil0,963

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,055
Tête enseignante GPT0,401
Écart entre enseignants0,346 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations8
Publié2017
Routes d'admission1
Résumé présentoui

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