A metasummary of published qualitative research on pregnancy and resettlement among refugee women
Bibliographic record
Abstract
Many authors have qualitatively explored the experiences of refugee women through pregnancy and childbirth, yet practitioners still strive to improve pre and postnatal health in this population. The purpose of this study was to use a qualitative metasummary process to identify commonalities and differences in these women’s experiences, and develop recommendations for practice. We used a systematic search to identify 23 relevant published research reports and independently rated the quality of each research report using a modified version of the Critical Appraisal Skills Programme (CASP). We classified 11 of 23 articles as at least good quality. The sample included participants from Europe, Asia, and parts of Africa, although the greatest proportion of research reports described Somalian participants. Primary resettlement countries included the US, Canada, and Australia. We used Dedoose software to extract themes and assess prevalence across contexts, and then determined credibility of findings based on the quality rating. Prevalent and credible themes included the Obstacles participants traversed in order to access appropriate and culturally sensitive healthcare, the ongoing process of Seeking Congruence between medical care and cultural traditions, and how women’s access to pre and postnatal healthcare was simultaneously Encouraging Resettlement. Healthcare practitioners might emphasize approaches to pre and postnatal care that encourage social connectedness, such as use of midwives or interpreters with expanded roles. Additionally, given the research focus we identified toward Somalian participants, we suggest that understanding of refugees’ experiences would be improved if researchers explored additional countries of origin and resettlement.
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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 | 0.086 | 0.241 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.021 |
| Bibliometrics | 0.035 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".