Birth Travellers Seeking Childbirth and Birthright Citizenship in Canada and the United States of America: A Qualitative Study with Nigerian Women
Bibliographic record
Abstract
Abstract Canada and the United States (USA) are top destinations for women travelling abroad for childbirth. However, end-to-end experiences of non-resident women travelling to seek childbirth and birthright citizenship in these destination countries are not fully understood, more so amongst Nigerian women, amongst whom the practice is particularly common. This study sought to address this gap in the literature. Twenty-five Nigerian women who had children in Canada or the USA were recruited via social media. In-depth interviews were conducted remotely, audio-recorded, transcribed, and analysed thematically. Five key themes emerged from this study including that the experience of visa application to travel abroad for childbirth was mixed; and the travel itself has recognised risks and can be stressful. Further, being pregnant did not mean women were treated well when they arrived at the entry port in the destination country. However, childbirth abroad was highly satisfactory for most, and obtaining birth certificates and passports for the babies was generally straightforward. Our findings underscore the necessity of clarifying the legality of seeking childbirth abroad and birthright citizenship. If deemed legal, the voices of women who opt to travel abroad for childbirth need to be elevated, their vulnerability recognised, and the quality of care they receive guaranteed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".