Tracking early visits to the dentist: a look at the first 3 years of the Manitoba Dental Association’s Free First Visit program.
Bibliographic record
Abstract
INTRODUCTION: In 2010, the Manitoba Dental Association launched its Free First Visit (FFV) program to provide dental screening for infants and toddlers. In this article, we review 3 years of FFV data submitted by participating dentists. METHODS: Data from tracking forms were reviewed for children≤36 months of age. These forms include the age of the child at the time of their FFV, their home postal code and caries status. Descriptive and bivariate analyses were carried out, and postal code geomapping was completed. RESULTS: Of the 8396 tracking forms submitted, 51.8% were for boys. The mean age at the time of the first visit was 24.2±7.8 months. Although only 8.5% had an FFV by 12 months, 26.7% had an FFV by 18 months. The average number of FFVs per month was 231.4±49.7. Postal code mapping revealed that participation was highest for children in the southern half of the province, including some high-needs neighbourhoods in Winnipeg. Pediatric dentists provided most FFVs and saw significantly younger children compared with general dentists (23.8±7.8 months of age vs. 25.2±7.7 months, p<0.001). CONCLUSIONS: Although many Manitoba children have had an FFV, few visit a dentist by 12 months, as recommended by the dental profession. There is a need to improve the proportion of children visiting a dentist by the recommended age, and general practitioners should assume a greater role in providing this service.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".