Stillbirth Among Arab Women in Canada, 1981-2015
Bibliographic record
Abstract
OBJECTIVES: The Arabic-speaking population is increasing in Europe and North America. Evidence suggests that Arab migrants have a greater risk of adverse birth outcomes than nonmigrants, but the risk of stillbirth is largely understudied. We examined inequality in stillbirth rates between Arab women and the French and English majority of women in Quebec, Canada. METHODS: We conducted a retrospective study of all births in Quebec from 1981 through 2015. We computed stillbirth rates by period and cause of death, and we used log binomial regression to estimate the association between Arabic mother tongue and stillbirth, adjusted for maternal characteristics. RESULTS: Stillbirth rates per 1000 births overall were lower among women with Arabic mother tongue (3.89) than among women with French or English mother tongue (4.52), and rates changed little over time. However, Arabic-speaking women from Arab countries had a higher adjusted risk of stillbirth than French- or English-speaking women (risk ratio = 1.23; 95% confidence interval, 1.07-1.42). Congenital anomalies, termination of pregnancy, and undetermined causes contributed to a disproportionate number of stillbirths among women with Arabic mother tongue compared with the French- and English-speaking majority. CONCLUSIONS: Arabic-speaking women from Arab countries have higher risks of stillbirth compared with the French and English majority in Quebec. Strategies to reduce stillbirth risk among Arabic speakers should focus on improving identification of causes of death.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| 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".