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Record W2888307755 · doi:10.1111/jphd.12283

Impact of public dental care spending and insurance coverage on utilization disparities among Canadian jurisdictions

2018· article· en· W2888307755 on OpenAlexaffabout
Armita Dehmoobadsharifabadi, Sonica Singhal, Carlos Quiñonez

Bibliographic record

VenueJournal of Public Health Dentistry · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsPublic Health OntarioUniversity of TorontoToronto Public Health
Fundersnot available
KeywordsDental insuranceMedicinePublic healthPer capitaDental careHealth careEnvironmental healthOddsPopulationOral healthDescriptive statisticsLogistic regressionFamily medicineGerontologyNursingEconomic growth

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the role of public dental care spending and insurance coverage on dental services utilization disparities among different Canadian jurisdictions. METHODS: We utilized Canadian Institute for Health Information provincial/territorial per capita estimates for public dental care expenditure, public information on legislated dental care programs, and oral health data from the 2007-2008 Canadian Community Health Survey to make inferences regarding the relationship between dentist visits in the past 12 months and self-perceived oral health. We performed descriptive statistics and binary logistic regression analysis to determine the relationship between dentist visits and self-perceived oral health status at the provincial/territorial level and on stratified data based on three age groups - children (12-17 years), adults (18-64 years), and seniors (65 years and older). RESULTS: Overall, the presence of the "inverse care law" in dental care at the provincial/territorial level was evident. However, in the Canadian territories, which had the highest per capita public dental care expenditure, individuals with poor oral health had the highest odds of visiting a dentist compared with other jurisdictions. In jurisdictions with public dental care programs for children and/or seniors, children and seniors with poor oral health were more likely to visit dentists. CONCLUSIONS: Jurisdictions with more public spending and greater population coverage for dental care appear to better cater to those with the highest oral health care needs.

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 score0.202
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.066
GPT teacher head0.373
Teacher spread0.307 · 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

Citations15
Published2018
Admission routes2
Has abstractyes

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