Perinatal Outcomes of Uninsured Immigrant, Refugee and Migrant Mothers and Newborns Living in Toronto, Canada
Bibliographic record
Abstract
Canadian healthcare insurance is not universal for all newcomer populations. New immigrant, refugee claimant, and migrant women face various barriers to healthcare due to the lack of public health insurance coverage. This retrospective study explored the relationships between insurance status and various perinatal outcomes. Researchers examined and compared perinatal outcomes for 453 uninsured and provincially insured women who delivered at two general hospitals in the Greater Toronto Area between 2007 and 2010. Data on key perinatal health indicators were collected via chart review of hospital medical records. Comparisons were made with regional statistics and professional guidelines where available. Four-in-five uninsured pregnant women received less-than-adequate prenatal care. More than half of them received clearly inadequate prenatal care, and 6.5% received no prenatal care at all. Insurance status was also related to the type of health care provider, reason for caesarean section, neonatal resuscitation rates, and maternal length of hospital stay. Uninsured mothers experienced a higher percentage of caesarian sections due to abnormal fetal heart rates and required more neonatal resuscitations. No significant difference was found for low birth weight, preterm birth, NCIU admissions, postpartum hemorrhage, breast feeding, or intrapartum care provided.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".