Increasing educational inequality in preterm birth in Québec, Canada, 1981–2006
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the relationship between preterm birth (PTB) and maternal education over time. We sought to determine whether educational inequalities in PTB have increased in Québec, Canada. METHODS: The authors analysed 2,124,909 singleton live births from 1981 to 2006, and computed the Relative Index of Inequality (RII) and Slope Index of Inequality (SII) with 95% CIs for the relationship between maternal education and extreme, very or moderate PTB (≤27, 28-31, and 32-36 completed weeks of gestation, respectively) for five periods (1981-1985, 1986-1990, 1991-1995, 1996-2000, 2001-2006), adjusting for maternal age, marital status, birthplace, language spoken at home, parity and infant sex. RESULTS: Average rates of extreme and moderate PTB increased over time but decreased for very PTB. A statistically significant increase in the RII over time was present for extreme and moderate PTB. The adjusted RII for extreme PTB increased from 1.58 (95% CI 1.24 to 2.01) in 1981-1985 to 3.11 (95% CI 2.54 to 3.81) in 2001-2006. For moderate PTB, the corresponding RIIs were 1.53 (95% CI 1.44 to 1.61) and 1.91 (95% CI 1.81 to 2.01). Absolute differences in the PTB proportion between the least and most educated mothers increased from 1981 to 2006 for extreme (adjusted SII 0.11% vs 0.28%) and moderate PTB (adjusted SII 1.67% vs 3.11%). Absolute differences in the proportion very PTB did not increase. CONCLUSIONS: Relative and absolute educational inequalities in extreme and moderate PTB have increased over time in Québec. Relative increases were largest for extreme PTB, and absolute increases were largest for moderate PTB.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".