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Canadian Dentists' Opinions on Publicly Financed Dental Care

2008· article· en· W2063181976 on OpenAlexafffundabout
Carlos Quiñonez, Rafael Figueiredo, David Locker

Bibliographic record

VenueJournal of Public Health Dentistry · 2008
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaHealth Canada
KeywordsRemunerationBusinessPrivate practiceDescriptive statisticsFamily medicineMedicinePublic relationsFinancePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to inform policy leaders of the opinions of Canada's major dental care service provider regarding publicly financed dental care. METHODS: Using provincial/territorial dental regulatory authority listings, a 26-item questionnaire was sent to a representative sample of Canadian dentists (n = 2219, response rate = 45.8 percent). Descriptive statistics were produced, and bivariate and multivariate logistic regressions were conducted to assess what predicts dentists' responses. RESULTS: Canadian dentists support governmental involvement in dental care, preferring investments in prevention to direct delivery. The majority of dentists have less than 10 percent of their practice represented by publicly insured patients, with a small minority having greater than 50 percent. The majority would accept new publicly insured patients, preferring fee for service remuneration. Dentists generally appear dissatisfied with public forms of third-party financing. CONCLUSIONS: Dentists prefer a targeted effort at meeting public needs and are influenced in their opinions largely in relation to ideology. In order to move forward, policy leaders will need to devote some attention to the influence and complexity of public and private tensions in dentistry At the very least, public and private practitioners must come to appreciate each other's challenges and balance public and private expectations in public programming.

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 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 score0.230
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.061
GPT teacher head0.350
Teacher spread0.289 · 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

Citations41
Published2008
Admission routes3
Has abstractyes

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