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Record W4210734421 · doi:10.1093/eurpub/ckaa165.984

20.F. Workshop: Dental care – coverage and access across countries

2020· article· en· W4210734421 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Public Health · 2020
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHealth carePublic healthPopulationEnvironmental healthHealth promotionOral healthScope (computer science)Dental careBusinessFamily medicineNursingEconomic growth

Abstract

fetched live from OpenAlex

Abstract Oral health is a central element of general health with significant impact in terms of pain, suffering, impairment of function and reduced quality of life. Although most oral disease can be prevented by health promotion strategies and routine access to primary oral health care, the GBD study 2017 estimated that oral diseases affect over 3.5 billion people worldwide (Watt et al, 2019). Given the importance of oral health and its potential contribution to achieving universal health coverage (UHC), it has received increased attention in public health debates in recent years. However, little is known about the large variations across countries in terms of service delivery, coverage and financing of oral health. There is a lack of international comparison and understanding of who delivers oral health services, how much is devoted to oral health care and who funds the costs for which type of treatment (Eaton et al., 2019). Yet, these aspects are central for understanding the scope for improvement regarding financial protection against costs of dental care and equal access to services in each country. This workshop aims to present the comparative research on dental care coverage in Europe, North America and Australia led by the European Observatory on Health Systems and Policies. Three presentations will look at dental care coverage using different methods and approaches. They will compare how well the population is covered for dental care especially within Europe and North America considering the health systems design and expenditure level on dental care, using the WHO coverage cube as analytical framework. The first presentation shows results of a cross-country Health Systems in Transition (HiT) review on dental care. It provides a comparative review and analysis of financing, coverage and access in 31 European countries, describing the main trends also in the provision of dental care. The second presentation compares dental care coverage in eight jurisdictions (Australia (New South Wales), Canada (Alberta), England, France, Germany, Italy, Sweden, and the United States) with a particular focus on older adults. The third presentation uses a vignette approach to map the extent of coverage of dental services offered by statutory systems (social insurance, compulsory insurance, NHS) in selected countries in Europe and North America. This workshop provides the opportunity of a focussed discussion on coverage of dental care, which is often neglected in the discussion on access to health services and universal health coverage. The objectives of the workshop are to discuss the oral health systems in an international comparative setting and to draw lessons on best practices and coverage design. The World Conference on Public Health is hence a good opportunity for this workshop that contributes to frame the discussion on oral health systems in a global perspective. Key messages There is large degree of variation in the extent to which the costs of dental care are covered by the statutory systems worldwide with implications for oral health outcomes and financial protection. There is a need for a more systematic collection of oral health indicators to make analysis of reliable and comparable oral health data possible.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.081
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0810.022

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 head0.070
GPT teacher head0.369
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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