Physical intimate partner violence and prenatal oral health experiences in the United States
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a significant public health issue, and when experienced during pregnancy, IPV substantially harms maternal health. Still, limited research has examined how IPV may influence prenatal oral health and dental care utilization. This study investigates the relationship between IPV during pregnancy and women's oral health experiences. DATA: Data are from 31 states from 2016-2019 in the United States that participated in the Pregnancy Risk Assessment Monitoring System (N = 85,289)-a population-based surveillance system of live births conducted annually by the Centers for Disease Control and Prevention and state health departments. Multivariable logistic regression analyses were used to examine the association between physical IPV during pregnancy (measured by being pushed, hit, slapped, kicked, choked, or physically hurt any other way by a current or ex-husband/partner) and various oral health experiences. FINDINGS: Women who experienced prenatal physical IPV reported worse oral health experiences during pregnancy, including being more likely to report not knowing it was important to care for their teeth, not talking about dental health with a provider, needing to see a dentist for a problem, going to see a dentist for a problem, as well as having more unmet dental care needs. CONCLUSIONS: Together, these findings indicate that women who experience physical IPV during pregnancy have lower knowledge of prenatal oral health care, more oral health problems, and greater unmet dental care needs. Given the risk of IPV and oral health problems for maternal and infant health, the study findings point to greater attention toward the oral health needs of IPV-exposed pregnant women.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".