Predictors of Unplanned Cesareans among Low‐Risk Migrant Women from Low‐ and Middle‐Income Countries Living in Montreal, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it