Intersection of Migration and Access to Health Care: Experiences and Perceptions of Female Economic Migrants in Canada
Bibliographic record
Abstract
More people are migrating than ever before. There are an estimated 1 billion migrants globally-of whom, 258 million are international migrants and 763 million are internal migrants. Almost half of these migrants are women, and most are of reproductive age. Female migration has increased. The socioeconomic contexts of women migrants need investigation to better understand how migration intersects with accessing health care. We employed a focused ethnography design. We recruited 29 women from three African countries: Ghana, Nigeria, and South Africa. We used purposive and convenient sampling techniques and collected data using face-to-face interviews. Interviews were audio-recorded and transcribed verbatim. Data were analyzed with the support of ATLAS.ti 8 Windows (ATLAS.ti Scientific Software Development GmbH), a computer-based qualitative software for data management. We interviewed 10 women from both South Africa and Ghana and nine women from Nigeria. Their ages ranged between 24 and 64 years. The four themes that developed included social connectedness to navigate access to care, the influence of place of origin on access to care, experiences of financial accessibility, and historical and cultural orientation to accessing health care. It was clear that theses factors affected economic migrant women's access to health care after migration. Canada has a universal health care system but multiple research studies have documented that migrants have significant barriers to accessing health care. Most migrants indeed arrive in Canada from a health care system that is very different than their country of origin. Access to health care is one of the most important social determinants of health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".