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Record W3214852428 · doi:10.1111/jphd.12486

Comparing the magnitude of oral health inequality over time in Canada and the United States

2021· article· en· W3214852428 on OpenAlexaffabout
Malini Chari, Vahid Ravaghi, Wael Sabbah, Noha Gomaa, Sonica Singhal, Carlos Quiñonez

Bibliographic record

VenueJournal of Public Health Dentistry · 2021
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsWestern UniversityPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsInequalityEdentulismRelative riskDemographyOral healthAbsolute (philosophy)MedicineMathematicsConfidence intervalSociologyDentistry

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the magnitude of, and changes in, absolute and relative oral health inequality in Canada and the United States, from the 1970s till the first decade of the new millennium. METHODS: Data were obtained from four national surveys; two Canadian (NCNS 1970-1972 and CHMS 2007-2009) and two American (HANES 1971-1974 and NHANES 2007-2008). The slope and relative index of inequality were used to measure absolute and relative inequality, respectively. Percentage change in inequality was also calculated. RESULTS: Relative inequality for untreated decay increased by 91% in Canada and 189% in the United States, while for filled teeth it declined by 63% in Canada and 16% in the United States. Relative inequality in edentulism rose by 200% and 78% in Canada and United States, respectively. Absolute inequality declined in both countries. CONCLUSIONS: There was persistent absolute and relative inequality in Canada and the United States. An increase in relative inequality for adverse outcomes suggests that improvements in oral health were occurring primarily among the rich, while reductions in relative inequality for filled teeth indicate higher utilization of restorative services among the poor. These results point to the necessity of tackling the sociopolitical determinants of health to mitigate oral health inequality in Canada and the United States.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.158
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.364
Teacher spread0.286 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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