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Dental service utilization by Europeans aged 50 plus

2011· article· en· W1579468116 on OpenAlexaff
Stefan Listl, Valérie Moran, Jürgen Maurer, Clóvis Mariano Faggion

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2011
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMedicineAttendanceIncidence (geometry)Dental careLogistic regressionDemographyService (business)Environmental healthGerontologyFamily medicineEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe variations in the utilization of dental services by persons aged 50+ from 14 European countries and to identify the extent to which such variations are attributable to differences in oral health need and in accessibility of dental care. METHODS: We use data from the Survey of Health, Ageing, and Retirement in Europe (SHARE Waves 2 and 3) and estimate a series of multivariate logistic regression models to analyze variations in dental service utilization (overall dental attendance, preventive treatment and/or operative treatment, dental attendance in early life years) RESULTS: Overall dental attendance and incidence of solely preventive treatment are comparatively high in the Netherlands, Sweden, Denmark, Germany, and Switzerland. In contrast, overall dental attendance is relatively low in Spain, Italy, France, Greece, Poland, and Ireland. Moreover, a high incidence of solely operative treatment is observed in Austria, Italy, and France, whereas in the Netherlands, Sweden, Denmark, Switzerland, and Ireland, the incidence of solely operative treatment is comparably low. By and large, these variations persist even when controlling for cross-country differences in oral health need and in accessibility of dental care. CONCLUSIONS: In comparison with other European regions, there is a tendency toward more frequent and preventive dental treatment of the elderly populations residing in Scandinavia and Western Europe. Such utilization patterns appear only partially attributable to differences in need for and accessibility of dental care.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.172
GPT teacher head0.381
Teacher spread0.209 · 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

Citations37
Published2011
Admission routes1
Has abstractyes

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