Adverse Childhood Experiences and Dental Care Utilization During Pregnancy: Findings from the North and South Dakota PRAMS, 2017-2021
Bibliographic record
Abstract
Background: Research demonstrates adverse childhood experiences (ACEs)-i.e., experiences of abuse, neglect, and household dysfunction-adversely impact healthcare utilization over the life course. Several studies demonstrate that ACEs are related to lower dental care utilization in childhood and adolescence. However, limited research has explored the connection between ACEs and dental care utilization in adulthood, and no research has examined this relationship during pregnancy. The current study extends existing research by investigating the relationship between ACEs and dental care utilization during pregnancy. Data: = 7,391). Multiple logistic regression is used to examine the relationship between the number of ACEs and dental care utilization. Findings: Relative to respondents with 0 ACEs, those with 4 or more ACEs were significantly less likely to report having dental care during pregnancy (OR = 0.745, 95% CI = .628, .883). By racial and ethnic background, the results showed that the significant associations are concentrated among White and Native American respondents. Conclusions: The results suggest that exposure to 4 or more ACEs is associated with a significantly lower likelihood of dental care utilization in adulthood, and this relationship is concentrated among White and Native American respondents. Further investigations are necessary to understand the mechanisms underlying the relationship between ACEs and dental care utilization and replicate the findings in other geographic contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".