Can a prenatal dental public health program make a difference?
Bibliographic record
Abstract
OBJECTIVE: Some pregnant women may be at increased risk of poor oral health. A publicly funded prenatal dental program in Vancouver, British Columbia, called Healthiest Babies Possible (HBP), has been providing oral health education and limited clinical services for over 20 years to low-income women assessed to be at high risk of preterm or low-weight births. This report is an assessment of the initial outcomes. METHODS: A prospective before-after evaluation of a non-probability convenience sample of women was undertaken over 1 year (2005-2006). Participants were seen at the customary 2 clinic visits, and were asked to return for a postnatal visit. Data collected by an inside evaluator, the program's dental hygienist, included questionnaires, semi-structured interviews, observations, clinical indices, appointment statistics and self-reports. Univariate and bivariate analyses (Student's t test and ANOVA) were performed. RESULTS: Of the 67 women in the sample, 61 agreed to participate; 36 (59%) attended all 3 appointments at the clinic, and 40 (66%) completed all 3 interviews and questionnaires either at the clinic or by telephone. Clinical indices of gingival health improved significantly over the time of the evaluation. Improvements in tooth cleaning were demonstrated by a significant decrease in plaque (p < 0.001). The proportion of the women's other children receiving professional dental care increased significantly (p < 0.001). Oral health knowledge improved and, overall, women expressed satisfaction with the program. CONCLUSION: Participants in this evaluation demonstrated improved gingival health, enhanced knowledge of oral health and positive tooth-cleaning behaviour. These women pursued infant oral care and sought professional dental visits for their 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".