MétaCan
Menu
Back to cohort
Record W3092016434 · doi:10.1093/eurpub/ckaa165.987

An extended comparative analysis using vignettes of dental care coverage

2020· article· en· W3092016434 on OpenAlexaffabout
Giovanni Fattore, Juliane Winkelmann, Димитра Пантели, Sonya Allin

Bibliographic record

VenueEuropean Journal of Public Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDental careStatutory lawMedicineHealth careOral health careFamily medicineOral healthPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Background Virtually all European countries state to guarantee universal healthcare coverage to their citizens. However, there is evidence, although scarce, that dental care is a special case as coverage is very limited in some countries and there are major differences across national jurisdictions. This comparative study tries to better understand how dental care is covered in Europe and Canada through the use of five vignettes presenting dental services for specific patients. Methods Experts from each country were asked to state whether the following situations were covered by their statutory systems: a) scaling and polishing to remove plaque deposits and calculus for an adult; b) treatment for caries including fillings and root canal treatment for an adult; c) prosthetic rehabilitation for an elderly; d) fillings and repairs for a seven-year old girl with serious tooth decay; e) orthodontic treatment for a 13 old boy. Results Preliminary analysis of the vignettes from 10 countries show that there is a large variation between countries in who delivers dental care, how much it costs and who pays for it. Conclusions Full coverage of dental care is rare in Europe and the extent of coverage varies greatly across countries in Europe and also in comparison to Canada.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.323
GPT teacher head0.550
Teacher spread0.227 · 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.

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

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of Public HealthSame topicDental Education, Practice, ResearchFrench-language works237,207