Predictors of Unplanned Cesareans among Low‐Risk Migrant Women from Low‐ and Middle‐Income Countries Living in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND: Research has yielded little understanding of factors associated with high cesarean rates among migrant women (i.e., women born abroad). The objective of this study was to identify medical, migration, social, and health service predictors of unplanned cesareans among low-risk migrant women from low- and middle-income countries (LMICs). METHODS: We used a case-control research design. The sampling frame included migrant women from LMICs living in Canada less than 8 years, who gave birth at one of three Montreal hospitals between March 2014 and January 2015. Data were collected from medical records and by interview-administration of the Migrant-Friendly Maternity Care Questionnaire. We performed multi-variable logistic regression for low-risk women (i.e., vertex, singleton, term pregnancies) who delivered vaginally (1,615 controls) and by unplanned cesarean indicated by failure to progress, fetal distress, or cephalopelvic disproportion (233 cases). RESULTS: Predictors of unplanned cesarean included being from sub-Saharan Africa/Caribbean (OR 2.37 [95% CI 1.02-5.51]) and admission for delivery during early labor (OR 5.43 [95% CI 3.17-9.29]). Among women living in Canada less than 2 years predictors were having a humanitarian migration classification (OR 4.24 [95% CI 1.16-15.46]) and admission for delivery during early labor (OR 7.68 [95% CI 3.12-18.88]). CONCLUSION: Migrant women from sub-Saharan Africa/Caribbean and recently arrived migrant women with a humanitarian classification are at greater risk for unplanned cesareans compared with other low-risk migrant women from LMICs after controlling for medical factors. Strategies to prevent cesareans should consider the circumstances of migrant women that may be contributing to the use of unplanned cesareans in this population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".