Knowledge, Attitudes and Practice Behaviour of Midwives Concerning Periodontal Health of Pregnant Patients
Bibliographic record
Abstract
Maternal gum disease is associated with adverse pregnancy outcomes such as preterm birth and low birthweight. This study aims to evaluate the knowledge, attitudes and practice behaviour of Australian midwives regarding the periodontal health of pregnant women to inform interprofessional antenatal care. This was an observational, cross-sectional study. We circulated an online questionnaire to Australian midwives from August 2018 to February 2019. Key outcome variables were knowledge, attitudes, and practice behaviours related to oral health. Key predictor variables were years of practice, practice location, and dental history. We summarized responses with frequency tables and assigned tallied scores for analysis using non-parametric statistical tests. 100 responses were analysed, including from rural (n = 23) and urban (n = 77) midwives. Eighty percent of midwives agreed that maternal dental care can positively affect pregnancy outcomes. Fluoridated toothpaste use (19.1%) was incorrectly answered to prevent gum disease more often than psychological stress control (7.9%), a correct answer. Rural midwives demonstrated a significantly higher knowledge score (p = 0.001) and significantly more positive practice behaviours towards oral health (p = 0.014) than urban midwives. Australian midwives have positive attitudes towards antenatal oral health but misunderstand gum disease aetiology and prevention. This study highlights areas to improve interprofessional education for optimal oral health and pregnancy outcomes.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".