Migrant-friendly maternity care in Montreal, Canada: A cross-sectional study on migrant women’s care perspectives
Bibliographic record
Abstract
OBJECTIVE: We assessed the extent to which recommended migrant-friendly maternity care (MFMC) components were provided to recently-arrived international migrants giving birth in Montreal, Canada, and the extent to which the provision of MFMC components was related to socioeconomic and migratory characteristics. METHODS: We conducted a cross-sectional study of migrant women giving birth in four hospitals in 2014-2015. Data were collected using the Migrant-Friendly Maternity Care Questionnaire (MFMCQ), focusing on access to prenatal care, communication facilitation, healthcare provider (HCP) support, and responsiveness to preferences for care. Data were analyzed descriptively and through logistic regression. RESULTS: Of 2636 participants, most reported always being kept informed (86.1%) and finding HCPs helpful (90.3%), although 22.9% reported barriers to accessing services during pregnancy, and only 11% or less were asked about care preferences. Of 847 needing interpreters, 84.7% reported not being offered any. Worse access to prenatal care was reported among women who had arrived more recently [OR 0.55, 95% CI 0.36, 0.85], had lower income [0.69 (0.52, 0.90)], or had less education [0.66 (0.47, 0.94)]. Low language ability was most often associated with inadequate MFMC [e.g., worse HCP support during pregnancy [0.56 (0.36, 0.87)] and worse responsiveness to preferences for care during labour [0.55 (0.31, 0.98)]]. Maternal region of birth was associated both positively and negatively with all MFMC components. CONCLUSION: Although some MFMC has been implemented, gaps remain. Addressing language barriers remains a top priority. To deliver optimal MFMC, HCPs and policymakers should provide care that is responsive to women's socioeconomic and migratory backgrounds.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".