Participation of childbearing international migrant women in research
Bibliographic record
Abstract
Fear of burdening or harming childbearing, migrant women, particularly refugees or others who have experienced war, torture, abuse, or rape, can result in their exclusion from research. This exclusion prohibits health issues and related solutions to be identified for this population. For this reason, while it may be challenging to include these women in studies, it is ethically problematic not to do so. Using ethical guidelines for research involving humans as a framework, and drawing on our research experiences. This discussion article proposes a number of strategies to improve the conditions for childbearing migrant women to participate in health research. What emerged as key for studying this diverse population and ensuring an ethically responsible approach are the use of methods that are adapted to the circumstances of childbearing migrant women and the involvement and support from "migrant-friendly" organizations. Ensuring migrant women are involved in the research process and knowledge produced is also critical. The more researchers working in this field communicate their experiences, the more will be learnt about how best to approach research with migrants. More migration and health research will enable a greater contribution to the knowledge base upon which the needs of this population can be met and their strengths maximized.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".