The Role of Interprofessional Education in Oral Health Promotion from Pregnancy to Early Childhood: Narrative Review
Bibliographic record
Abstract
Abstract Oral health is integral to general health and vice versa and should be viewed as such. Therefore, interprofessional collaboration in oral health is essential for enhancing overall systemic health, preventing dental diseases, and promoting oral health awareness among expectant families and young children. This narrative review provides an overview of critical aspects of interprofessional collaboration in promoting oral health from pregnancy to early childhood. A comprehensive search using electronic databases was conducted for publications from 2014 to 2024. The authors included studies that assessed the role of interprofessional education (IPE) and practices in oral health care and recommended the best IPE practices to achieve optimal oral health in children. Several oral health conditions may occur during pregnancy. Yet, many pregnant women are unaware of the importance of prenatal and postnatal oral care and that seeking regular or emergency dental care is safe. Interprofessional collaboration in oral health promotion needs to be improved in many ways to help increase awareness and address dental problems for pregnant women and young children. IPE and practice can enable effective communication between health care professionals and critically contribute to optimal oral health in children and overall well-being in pregnancy and early childhood.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".