Longitudinal study of factors predicting toothbrushing less than twice daily at age 2 years in the FinnBrain Birth Cohort Study
Bibliographic record
Abstract
We aimed to identify factors predicting toothbrushing less than twice daily at the age of 2 years. The data from the FinnBrain Birth Cohort Study of 506 mother-father pairs (with 506 children) were used. Logistic regression analyses were conducted of the outcome (brushing less than twice daily) at the age of 24 months. Independent variables were parental age, parental education, siblings in the household, use of childcare service, and information about whether the parents are divorced, as well as brushing of the child's teeth and the parents' own teeth at 12 months with their first order interactions. More than one quarter of the parents reported brushing their child's teeth less than twice daily at the age of 2 years. The strongest predictor for brushing the child's teeth less than twice daily at the age of 24 months was brushing child's teeth less than twice daily at the age of 12 months; the effect was significantly stronger for those children whose fathers had low education than for those whose fathers had medium/high education. Other predictors were mother's and father's own brushing at 12 months, childcare at home, and mother's low education. To improve toothbrushing in young children, early intervention is needed in families where parents brush their own teeth less than twice daily and in families with low education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".