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Lack of Oral Care Policies in Toronto Daycares

2009· article· en· W1970352045 on OpenAlexaffabout
Elena Gartsbein, Herenia P. Lawrence, James L. Leake, Hazel Stewart, Gajanan Kulkarni

Bibliographic record

VenueJournal of Public Health Dentistry · 2009
Typearticle
Languageen
FieldHealth Professions
TopicPediatric health and respiratory diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOral hygieneMedicineTooth brushingOral healthFamily medicineTest (biology)Dental carePopulationDentistryNursingEnvironmental healthToothbrush

Abstract

fetched live from OpenAlex

OBJECTIVES: Currently, there is a deficit of information on policies regarding oral hygiene practices in Toronto daycares. It is unknown if any tooth-brushing programs are in existence and if children are permitted to follow professional advice on oral hygiene. The main objectives of this investigation were to a) determine the prevalence of oral care policies in daycares and b) examine the availability of resources. METHODS: Telephone interviews were conducted with daycare supervisors using a pretested questionnaire. Summary statistics and the chi-square test were used to analyze the results. RESULTS: Two hundred forty-nine questionnaires were completed (response rate of 99.6 percent), representing 38 percent of the total daycare population (650) in Toronto. Eighty-three percent did not have a policy on oral care and 11 percent would not cede to requests from parents or medical professionals to brush teeth. However, 50 daycares indicated that their centers used to have a tooth-brushing program, and most (79 percent) were open to establishing an oral care policy. Fifteen percent reported not having proper sinks for tooth brushing. CONCLUSIONS: Many daycares do not have a policy regarding oral hygiene. A policy that encourages and provides guidance on safe tooth-brushing procedures is needed and may improve the oral health of preschool children.

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)0.003
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 score0.113
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.185
GPT teacher head0.529
Teacher spread0.344 · 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

Citations3
Published2009
Admission routes2
Has abstractyes

Explore more

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