Predictors of preconception health knowledge among Canadian women: A nationwide cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Optimising preconception health-that is the health of women and men prior to a potential pregnancy-is increasingly recognised as fundamental to improving maternal and infant health outcomes. To date, limited research has been conducted examining preconception knowledge and studies focusing on preconception health behaviours have targeted certain behaviours, while overlooking others, with limited attention given to the interconception period and differences between multiparous and primiparous/nulliparous women. AIMS: To determine predictors of preconception health knowledge among Canadian women and to examine whether parity modified the effect of predictors on preconception knowledge. MATERIALS AND METHODS: A cross-sectional study reported according to STROBE was undertaken from May to June 2019 in Canada with 928 women. An online questionnaire was used including the Preconception Health Knowledge Questionnaire, demographic characteristics, current health status, previous pregnancy outcomes and use of preconception care services. Ordinary least squares regression was used to model knowledge scores. Predictors were entered using theoretically driven hierarchical entry. RESULTS: Mean age of women was 34 years and one in five were immigrants. In the final model, household income (b = .17, SE = .07; p = .009), being born outside Canada (b = -.75, SE = .25; p = .003), miscarriage/stillbirth history (b = .47, SE = .21; p = .027) and previous use of preconception care (b = .97, SE = .20, p ⟩ .001) were predictive of preconception health knowledge. Effect modification by parity was not statistically significant in the final model (f = 1.22, p = .19). DISCUSSION: Women at higher risk of poor preconception knowledge, and who therefore stand to gain from preconception knowledge interventions may include those who (1) are socially and economically disadvantaged; (2) have not engaged in preconception care previously and (3) were not born in Canada. Ensuring national promotion of and access to preconception care is an important strategy to prevent adverse pregnancy outcomes and optimise maternal and infant health. CONCLUSION: This study highlights the need for national promotion of and access to preconception health care for all pregnancy-planning families in order to improve perinatal outcomes. RELEVANCE FOR CLINICAL PRACTICE: When evaluating preconception health efforts, preconception health knowledge must be considered within the context of social determinants of health and individuals' abilities to act on their knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".