Immigrant and non-immigrant womenâs experiences of maternity care: a systematic and comparative review of studies in five countries:
Bibliographic record
Abstract
Background Understanding immigrant women’s experiences of maternity care is critical if receiving country care systems are to respond appropriately to increasing global migration. This systematic review aimed to compare what we know about immigrant and non-immigrant women’s experiences of maternity care. Methods Medline, CINAHL, Health Star, Embase and PsychInfo were searched for the period 1989–2012. First, we retrieved population-based studies of women’s experiences of maternity care (n = 12). For countries with identified population studies, studies focused specifically on immigrant women’s experiences of care were also retrieved (n = 22). For all included studies, we extracted available data on experiences of care and undertook a descriptive comparison. Results What immigrant and non-immigrant women want from maternity care proved similar: safe, high quality, attentive and individualised care, with adequate information and support. Immigrant women were less positive about their care than non-immigrant women. Communication problems and lack of familiarity with care systems impacted negatively on immigrant women’s experiences, as did perceptions of discrimination and care which was not kind or respectful. Conclusion Few differences were found in what immigrant and non-immigrant women want from maternity care. The challenge for health systems is to address the barriers immigrant women face by improving communication, increasing women’s understanding of care provision and reducing discrimination.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".