Stressful life events, oral health, and barriers to dental care during pregnancy
Bibliographic record
Abstract
OBJECTIVES: Poor oral health during pregnancy poses risks to maternal and infant well-being. However, limited research has documented how proximate stressful life events (SLEs) during the prenatal period are associated with oral health and patterns of dental care utilization. METHODS: Data come from 13 states that included questions on SLEs, oral health, and dental care utilization in the Pregnancy Risk Assessment Monitoring System for the years 2016-2020 (n = 48,658). Multiple logistic regression analyses were used to assess the association between levels of SLE (0, 1-2, 3-5, or 6+) and a range of (1) oral health experiences and (2) barriers to dental care during pregnancy while controlling for socio-demographic and pregnancy-related characteristics. RESULTS: Women with more SLEs in the 12 months before birth-especially six or more-reported worse oral health experiences, including not having dental insurance, not having a dental cleaning, not knowing the importance of caring for teeth and gums, needing to see a dentist for a problem, going to see a dentist for a problem, and unmet dental care needs. Higher levels of SLEs were also associated with elevated odds of reporting barriers to dental care. CONCLUSIONS: SLEs are an essential but often understudied risk factor for poor oral health, unmet dental care needs, and barriers to dental care services. Future research is needed to understand better the mechanisms linking SLEs and oral health.
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".