Medical, behavioural and social preconception and interconception risk factors among pregnancy planning and recently pregnant Canadian women
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to describe the clustering of medical, behavioural and social preconception and interconception health risk factors and determine demographic factors associated with these risk clusters among Canadian women. DESIGN: Cross-sectional data were collected via an online questionnaire assessing a range of preconception risk factors. Prevalence of each risk factor and the total number of risk factors present was calculated. Multivariable logistic regression models determined which demographic factors were associated with having greater than the mean number of risk factors. Exploratory factor analysis determined how risk factors clustered, and Spearman's r determined how demographic characteristics related to risk factors within each cluster. SETTING: Canada. PARTICIPANTS: Participants were recruited via advertisements on public health websites, social media, parenting webpages and referrals from ongoing studies or existing research datasets. Women were eligible to participate if they could read and understand English, were able to access a telephone or the internet, and were either planning a first pregnancy (preconception) or had ≥1 child in the past 5 years and were thus in the interconception period. RESULTS: Most women (n=1080) were 34 or older, and were in the interconception period (98%). Most reported risks in only one of the 12 possible risk factor categories (55%), but women reported on average 4 risks each. Common risks were a history of caesarean section (33.1%), miscarriage (27.2%) and high birth weight (13.5%). Just over 40% had fair or poor eating habits, and nearly half were not getting enough physical activity. Three-quarters had a body mass index indicating overweight or obesity. Those without a postsecondary degree (OR 2.35; 95% CI 1.74 to 3.17) and single women (OR 2.22, 95% CI 1.25 to 3.96) had over twice the odds of having more risk factors. Those with two children or more had 60% lower odds of having more risk factors (OR 0.68, 95% CI 0.52 to 0.86). Low education and being born outside Canada were correlated with the greatest number of risk clusters. CONCLUSIONS: Many of the common risk factors were behavioural and thus preventable. Understanding which groups of women are prone to certain risk behaviours provides opportunities for researchers and policy-makers to target interventions more efficiently and effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".