Quantile regression analysis of language and interpregnancy interval in Quebec, Canada
Bibliographic record
Abstract
INTRODUCTION: Short and long interpregnancy intervals are associated with adverse perinatal outcomes such as miscarriage and preterm delivery, but cultural differences in interpregnancy intervals are understudied. Identifying cultural inequality in interpregnancy intervals is necessary to improve maternal-child outcomes. We assessed interpregnancy intervals for Anglophones and Francophones in Quebec. METHODS: We obtained birth records for all infants born in Quebec, 1989-2011. We identified 571 461 women with at least two births, and determined the interpregnancy interval. We defined short interpregnancy intervals (< 18 months) as the 20th percentile of the distribution, and long intervals (≥ 60 months) as the 80th percentile. Using quantile regression, we evaluated the association of language with short and long intervals, adjusted for maternal characteristics. We assessed differences over time and by maternal age for disadvantaged groups defined as no high school diploma, rural residence, and material deprivation. RESULTS: In adjusted regression models, Anglophones who had no high school diploma had intervals that were 1.0 month (95% CI: -1.5 to -0.4) shorter than Francophones at the 20th percentile of the distribution, and 1.9 months (-0.5 to 4.3) longer at the 80th percentile. Results were similar for Anglophones in rural and materially deprived areas. The trends persisted over time, but were stronger for women < 30 years. There were no differences between advantaged Anglophones and Francophones. CONCLUSION: Disadvantaged Anglophones are more likely to have short and long interpregnancy intervals relative to Francophones in Quebec. Public health interventions to improve perinatal health should target suboptimal intervals among disadvantaged Anglophones.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".