An exploratory study of pregnant women's knowledge of child oral health care in New Zealand.
Bibliographic record
Abstract
BACKGROUND: To be maximally effective, oral health preventive strategies should start at birth. There appear to be few reports on pregnant women's knowledge of oral health care for their developing children. OBJECTIVES: This exploratory study assessed Dunedin expectant mothers' knowledge of the oral health care of their future children. METHODS: A questionnaire was developed to assess expectant mothers' knowledge of child oral health and appropriate prevention strategies. Three public Lead Maternity Carer (LMC) organisations and 30 private individual LMCs were asked to distribute the questionnaire to their clients attending appointments during a one-month period. Questions focused on the mother's knowledge of oral health practices for their future children, including oral hygiene and access to dental care. RESULTS: Fewer than half of the participants thought they had enough information about their child's oral health needs. One-quarter thought that toothbrushing should not start until after two years of age. The majority thought their child should not be seen by a dental professional until this age, while one-fifth did not think their child should be seen until four years old. Poorer child oral health knowledge was found in first-time mothers, younger women, those from low-SES groups, and those who were not New Zealand (NZ) Europeans. CONCLUSIONS: A substantial number of participants were unaware of how to provide appropriate oral health care for their children despite the available information. This lack of awareness needs to be taken into account when designing oral health promotion strategies for parents of very young children.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".