Induced abortion according to immigrants’ birthplace: a population-based cohort study
Bibliographic record
Abstract
Abstract Background Most abortions occur due to unintended pregnancy. Unintended pregnancies are linked to poor health outcomes. Canada receives immigrants from countries with disparate sexual and reproductive health contexts which may influence abortion rates post-migration. We examined the association between abortion and region of birth and birth order among Canadian immigrants. Methods We conducted a population-based person-years (PY) cohort study in Ontario, Canada using administrative immigration (1991–2012) and health care data (1991–2013). Associations between induced abortion and an immigrant’s region of birth were estimated using poisson regression. Rate ratios were adjusted for age, landing year, education, neighborhood income quintile and refugee status and stratified by birth order within regions. Results Immigrants born in almost all world regions (N = 846,444) were 2–5 times more likely to have an induced abortion vs. those born in the US/Northern & Western Europe/Australia & New Zealand (0.92 per 100 PY, 95% CI 0.89–0.95). Caribbean (Adjusted Rate Ratio [ARR] = 4.71, 95% CI 4.55–4.87), West/Middle/East African (ARR = 3.38, 95% CI 3.26–3.50) and South American (ARR = 3.20, 95% CI 3.09–3.32) immigrants were most likely to have an abortion. Most immigrants were less likely to have an abortion after vs. prior to their 1st birth, except South Asian immigrants (RR = 1.60, 95% CI 1.54–1.66; RR = 2.23, 95% CI 2.12–2.36 for 2nd and 3rd vs 1st birth, respectively). Secondary analyses included further stratifying regional models by year, age, education, income quintile and refugee status. Conclusions Induced abortion varies considerably by both region of birth and birth order among immigrants in Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".