Integrating oral health into prenatal care: a scoping review
Bibliographic record
Abstract
Purpose Integrating preventive oral care into prenatal care is suggested as a strategy for reducing the burden of oral diseases among pregnant women and their offspring. This scoping review sought to synthesize available information and identify knowledge gaps on integrating oral health into prenatal care. Design/methodology/approach The scoping review was conducted based on the Joanna Briggs Institute scoping review framework using the following databases: CINAHL, Cochrane Database of Systematic Reviews, Medline, ProQuest Dissertation and theses Global, Psychinfo and Web of Science®. No search limits were used. Content analysis of the included articles was performed to identify conceptual frameworks, types of integration used, study designs, study objectives and outcomes. Findings Overall, 2,861 references were obtained from the databases search; and based on the inclusion and exclusion criteria 35 references were included in the final analysis. Of these 35 references, one document presented a conceptual model, six documents reviewed guidelines for integrating oral health in prenatal care, two were policy documents aimed at interprofessional collaboration for oral health during pregnancy, eight documents described programs focused on providing oral care during pregnancy, five of the references were literature reviews and the remaining 13 evaluated the impact of integration. Linkages between healthcare professionals were the most common type of integration used. Research limitations/implications Despite advances in understanding integrated care concepts for healthcare delivery, there is little evidence available on the impact of the various types of, and strategies for, integrating oral health into prenatal care. Future research to bridge the identified gaps is recommended. Originality/value The originality of this study is to provide evidence on integrated oral healthcare during pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".