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Self‐reported cost‐prohibitive dental care needs among Canadians

2012· article· en· W1552269309 on OpenAlexaffabout
Chantel Ramraj, C. Quiñonez

Bibliographic record

VenueInternational Journal of Dental Hygiene · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineLogistic regressionDescriptive statisticsDenturesDental careDental insuranceOral healthFamily medicineEnvironmental healthDentistry

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore self-reported cost-prohibitive dental treatment needs among Canadians. METHODS: Data were collected through a national telephone interview survey of 1006 randomly selected Canadian adults. Descriptive analyses based on socio-demographic characteristics and dental-related behaviours were undertaken. Logistic regression was used to determine the predictors of experiencing a cost-prohibitive dental care need. Chi-square tests were used to determine significant differences in the treatments reported as unaffordable by socio-demographic characteristics and dental-related behaviours. RESULTS: Those of low income, no insurance coverage and poor self-rated oral health were more likely to report having a cost-prohibitive dental care need. The top needs reported as unaffordable were fillings, cleanings and check-ups. Comparatively, preventive services were selected as cost-prohibitive more often by the insured, dentures by the oldest group and extractions by those with a high school education or less. CONCLUSIONS: This study confirms that there are significant relationships between socio-demographic factors, dental-related behaviours and the types of dental services that are selected as unaffordable. Indirectly, this shows us how socio-demographic factors may influence the types of dental services that are reported as 'needed' by certain groups. Difficulties in distinguishing between the services that are 'needed' from and those that are 'wanted' demonstrate some of the policy complexity associated with publicly financed 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)0.000
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 score0.052
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.301
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.

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

Citations37
Published2012
Admission routes2
Has abstractyes

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