Factors Affecting Utilization of Dental Services During Pregnancy
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to identify and evaluate factors affecting utilization of dental services during pregnancy. METHODS: Participants in this cross-sectional study were mothers visiting a community health center for their infants'/toddlers' immunization. Data were collected through a questionnaire about demographics, oral health knowledge, attitude, and practices, as well as barriers to dental visits during pregnancy. Mean (SD) and frequencies were used for data description. Different factors were analyzed as predictors for utilization of dental services using multiple logistic regression analysis. RESULTS: In total, 423 mothers completed the study. Mean (SD) age at delivery was 29.5 (5.3) years. Almost all participants brushed their teeth at least once daily with toothpaste. During pregnancy, 19.2% of mothers reported difficulties with brushing, and 25% had dental/periodontal problems. Half of the participants had a dental visit during pregnancy; 93% were for dental checkups, 80.5% received preventive care, and 28.8% received dental/periodontal treatments. Canadian-born women were 48% more likely to visit the dentist during pregnancy compared with non-Canadian counterparts (P = 0.048). Level of education, dental insurance, and household income were also positively associated with usage (P <0.001). Mothers with more knowledge about possible connections between oral health and pregnancy and those who visited the dentist every 6 months had better odds of visiting the dentist during pregnancy (P <0.001). CONCLUSION: Three major factors predicting the utilization of dental services during pregnancy were: 1) perceived need, 2) habit of regular dental visits, and 3) access to dental services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".