Lack of Oral Care Policies in Toronto Daycares
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".