The influence of maternal and paternal education on birth outcomes: an analysis of the Ottawa and Kingston (OaK) birth cohort
Bibliographic record
Abstract
Background Education is considered one of the most robust determinants of health. However, it is unclear whether maternal education and paternal education have differential impacts on perinatal health outcomes. We assess maternal and paternal education differences and their association with adverse birth outcomes in a large birth cohort from Ontario, Canada.Methods The OaK Birth Cohort recruited patients from Ontario, Canada, between October 2002 and April 2009. We recruited mothers were recruited between 12 and 20 weeks’ gestation and collected both mother and infant data. The final sample size of the cohort was 8,085 participants. We use logistic regression to model the probability of preterm birth (less than 34 and 37 weeks’ gestation), small-for-gestational-age (SGA), or stillbirth as a function of maternal and paternal educational attainment. We adjust for household-level income, maternal and paternal race and ethnicity, and compare the strength of the association between maternal and paternal education on outcomes using Wald tests.Results 7,928 mother-father-offspring triads were available for the current analysis. 75% of mothers and fathers had college or university level education, and 8.7% of mothers experienced preterm delivery. Compared to mothers with college or university education, mothers with a high school education had an odds ratio of 1.37 (95% CI: 1.01–1.87) for SGA. Paternal education was not associated with infant outcomes. Comparing the odds ratios for maternal education and paternal education showed a stronger association than paternal education at the high school level for SGA birth (difference in odds ratio: 1.95, 95% CI: 1.13–3.36, p = .016) among women at least 25 years old.Conclusion Maternal education was associated with SGA, and this effect was more robust than paternal education, but both associations were weaker than previously reported.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".