Transnational prenatal care among migrant women giving birth in Montreal, Canada
Bibliographic record
Abstract
Abstract Background Given the number of barriers that migrant women confront while accessing maternity care in receiving countries, it is expected that they may engage in and rely on transnational healthcare to maintain their health and well-being during pregnancy. The objective of this study was to estimate the prevalence and identify predictors of transnational prenatal care (TPC) (i.e., prenatal care from a healthcare professional in more than one country) among migrant women from low- and middle-income countries (LMICs) who gave birth in Montreal, Canada. Methods We conducted a secondary analysis (n = 2595 women) using data from the Migrant-Friendly Maternity Care (MFMC) - Montreal project. The MFMC study used a cross-sectional design. Data were gathered from recently-arrived (< 8 years) migrant women from LMICs via medical record review and interview-administration of the MFMC questionnaire postpartum during the period of March 2014-January 2015 in three hospitals, and February-June 2015 in one hospital. We used logistic regression modelling to identify socio-demographic, migration, health, and perceptions of care variables significantly associated to TPC (referent group were women who only received prenatal care in Canada). Results Four percent of women reported receiving TPC. Sixty-one percent of TPC women were from the Middle East/North Africa (vs. 42% among no-TPC women); top source countries were Tunisia, Algeria, Morocco, Lebanon, Egypt and Iran. Predictors of TPC included: ‘Not living with the father of the baby’ (AOR = 4.8, 95%CI 2.4, 9.8), ‘having negative perceptions of pregnancy care in Canada (general experiences)’ (AOR = 1.2, 95%CI 1.1, 1.3) and younger maternal age (AOR = 1.1, 95%CI 1.0, 1.1). Conclusions Migrant women may be motivated to return to the home country for healthcare during pregnancy due to a need for family and social support and/or to overcome access barriers and negative pregnancy care experiences in the receiving country. Key messages • To reduce the potential for harm and to ensure a continuity of care, healthcare providers should inquire about care, support and other services received outside of the country during pregnancy. • Migrant women seeking care in their home country may address inadequacies regarding support and cultural preferences in prenatal care delivered in the receiving-country.
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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 | 0.003 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".