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Record W2030777795 · doi:10.1155/2013/602791

Dental Treatment Needs in Vancouver Inner-City Elementary School-Aged Children

2013· article· en· W2030777795 on OpenAlexaffabout
Firoozeh Samim, Jolanta Aleksejūnienė, Christopher Zed, Nooshin Salimi, C. P. Emperumal

Bibliographic record

VenueInternational Journal of Dentistry · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDental careFamily medicineDemographyDental healthInner cityCensusFamily incomeDentistryGerontologyPediatricsEnvironmental healthGeographyPopulation

Abstract

fetched live from OpenAlex

Aims. To examine the dental treatment needs of inner-city Vancouver elementary school-aged children and relate them to sociodemographic characteristics. Methods. A census sampling comprising 562 children from six out of eight eligible schools was chosen (response rate was 65.4%). Dental treatment needs were assessed based on criteria from the World Health Organization. Results. Every third child examined needed at least one restorative treatment. A higher proportion of children born outside Canada were in need of more extensive dental treatments such as pulp care and extractions compared to the children born in Canada. There were no statistically significant differences in dental treatment needs between age, gender, or income groups or between children with or without dental insurance (Chi Squared P > 0.05). The best significant predictors (Linear Multiple Regression, P > 0.05) of higher dental treatment needs were being born outside Canada, gender, time of last dental visit, and family income. Having dental insurance did not associate with needing less treatment. Conclusion. A high level of unmet dental treatment needs (32%) was found in inner-city Vancouver elementary school-aged children. Children born outside Canada, particularly the ones who recently arrived to Canada, needed more extensive dental treatments than children born in Canada.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.299
Teacher spread0.288 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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