MétaCan
Menu
Back to cohort
Record W2755174194 · doi:10.22605/rrh3809

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

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

Bibliographic record

VenueRural and Remote Health · 2017
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
FundersAustralian GovernmentAustralian Primary Health Care Research Institute, Australian National UniversityPrimary Health Care Research, Evaluation and Development
KeywordsHealth policyRural areaGovernment (linguistics)Rural healthEconomic growthPovertyHealth careMedicinePublic healthEnvironmental healthPolitical scienceNursing

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.401
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueRural and Remote HealthSame topicDental Health and Care UtilizationFrench-language works237,207