Migrant Friendly Maternity Care in a Western Urban Centre
Bibliographic record
Abstract
Background and Purpose Migrant-sensitive care provision has been identified as a priority in the World Health Assembly Resolution, ‘Health of Migrants’. Little research has been done on the extent to which migrant-sensitive (‘friendly’) maternity care (MFMC) is currently being provided, factors that support or inhibit provision of such care, and whether specific components of MFMC may be more important than others. We sought to determine: (1) to what extent recommended components of MFMC are being provided to recently-arrived international migrant women giving birth in an urban Canadian city; and (2) what contextual factors support the implementation of MFMC. Methodology We conducted a mixed quantitative-qualitative study of 2400 women recently giving birth, speaking any language, in Canada <8 years, and from non-Western countries; and 63 health professionals. Medical records and unit documents were reviewed. The Migrant Friendly Maternity Care Questionnaire was administered and open-ended interviews were completed. Results Women from over 97 countries, speaking any of 79 languages reported on their perceptions of how the health system responded to their needs including communication facilitation, promotion of social support, education for healthy weight, treatment of pre-pregnancy/perinatal/maternal illnesses, early access to prenatal care, and responsiveness to preferences for care, among other indicators of MFMC. A range of professionals reported on challenges to care provision and how these were met. Conclusions and Discussion Empirical data on migrant-sensitive maternity care, contextual factors supportive of that care, and associated reproductive health outcomes offer baseline data for programming and to permit benchmarking nationally and internationally. Key messages Empirical data on migrant-sensitive maternity care offer baseline data for health programming Migrant-friendly maternity care data allow for benchmarking for the future
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".