Self-Perceived Oral Health and Use of Dental Services by Pregnant Women in Surrey, British Columbia.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the self-reported oral health status and needs and the patterns of use of dental services by a sample of pregnant women from diverse ethnic backgrounds in the city of Surrey, British Columbia, Canada. METHOD: A 34-item cross-sectional survey was administered to women enrolling in a prenatal program for 4 months in 2012/13. For data analysis, we used a 2-sample t test and tested categorical variables using a χ2 test. We used multivariable logistic regression analysis to estimate the odds ratio for the variables, self-reported oral health status and use of dental services. RESULTS: Of the 740 pregnant women who participated in this survey (87% of registrants), 30% were considered vulnerable because of inability to live within their household income, smoking status, self-reported depression, lack of dental insurance and time since last dental visit. Most respondents (84%) rated their oral health good or excellent. Almost half of the women had not visited a dental professional during the past year, while 23% saw a dental professional only for emergency purposes. Women with dental insurance were 6.6 times more likely to have visited a dental professional than those without insurance. CONCLUSION: Although most pregnant women considered dental care during pregnancy to be important, almost half had not visited a dental professional during the pregnancy.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".