Pregnant women’s perspectives on integrating preventive oral health in prenatal care
Bibliographic record
Abstract
BACKGROUND: Oral diseases are considered a silent epidemic including among pregnant women. Given the prevalence of oral conditions among pregnant women and the reported association with adverse pregnancy outcomes, there have been suggestions for the inclusion of preventive oral care in routine prenatal care. However, due to the different administrative and funding structure for oral health and prenatal care in Canada, progress towards this integration has been slow. Our study sought to qualitatively explore the views of pregnant women in British Columbia (BC) on the strategies for integrating preventive oral health care into prenatal care services. METHODS: A qualitative approach was utilized involving semi-structured interviews with fourteen (14) purposefully selected pregnant women in Vancouver and Surrey, BC. The interviews were audio-recorded and transcribed. The transcripts were analyzed using an inductive thematic approach. Study validity was ensured via memoing, field-notes, and member checking. RESULTS: Interviews ranged from 28 to 65 min producing over 140 pages of transcripts. Analysis resulted in three major themes: oral health experiences during pregnancy, perspectives on integration and integrated prenatal oral care, and strategies for addressing prenatal oral health care. A majority of participants were supportive of integrating preventive oral care in routine prenatal services, with referrals identified as a critical strategy. Oral health education was recognized as important before, during, and after pregnancy; oral health assessments should therefore be included in the prenatal care checklist. Limited funding was acknowledged as a barrier to oral health care access, which may explain why few participants visited their dentists during pregnancy. Interprofessional education surfaced as a bridge to provide prenatal oral health education. CONCLUSION: Pregnant women interviewed in this study support the inclusion of educational and preventive oral care during prenatal care, although their views differed on how such inclusion can be achieved in BC. They advocated the establishment of a referral system as an acceptable strategy for providing integrated prenatal oral health care.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".