Challenges newly-arrived migrant women in Montreal face when needing maternity care: Health care professionals’ perspectives
Bibliographic record
Abstract
BACKGROUND: People who leave their country of origin, or the country of habitual residence, to establish themselves permanently in another country are usually referred to as migrants. Over half of all births in Montreal, Canada are to migrant women. To understand healthcare professionals' attitudes towards migrants that could influence their delivery of care, our objective was to explore their perspectives of challenges newly-arrived migrant women from non-Western countries face when needing maternity care. METHOD: In this qualitative multiple case study, we conducted face-to-face interviews with 63 health care professionals from four teaching hospitals in Montreal, known for providing maternity care to a high volume of migrant women. Interviews were transcribed and thematically analysed. RESULTS: Physicians, nurses, social workers, and therapists participated; 90% were female; and 17% were themselves migrants from non-Western countries. According to participants, newly-arrived migrant women face challenges at two levels: (a) direct care (e.g., understanding Canadian health care professionals' expectations, communicating effectively with health care professionals), and (b) organizational (e.g., access to appropriate health care). Challenges women face are strongly influenced by the migrant woman's background as well as social position (e.g., general education, health literacy, socio-cultural integration) and by how health care professionals balance women's needs with perceived requirement to adhere to standard procedures and regulations. CONCLUSIONS: Health care professionals across institutions agreed that maternity care-related challenges faced by newly-arrived migrant women often are complex in that they are simultaneously driven by conflicting values: those based on migrant women's sociocultural backgrounds versus those related to the implementation of Canadian guidelines for maternity care in which consideration of migrant women's particular needs are not priority.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".