Translating Priorities Into Practice: Midwifery Care for Uninsured Migrant Populations Across Canada
Bibliographic record
Abstract
BACKGROUND: Immigrants and newcomers are identified by many provincial midwifery associations as "priority populations." Recently, newcomer populations have shifted considerably, with more people coming to Canada with precarious immigration status who are increasingly ineligible for public healthcare insurance and facing barriers to accessing care. Our aims were to: (1) gain an understanding of the policies related to equitable access to midwifery care and how they may apply to migrant groups without public healthcare insurance and (2) identify existing policy themes, gaps, and regulatory barriers that limit access for this vulnerable population in Canada. METHODS: We conducted a high-level document content analysis using a health equity framework. We aimed to identify language related to equitable access in midwifery services, with particular emphasis on uninsured populations. A total of 64 documents were analyzed, including legislation and publicly available statements from midwifery regulatory bodies and associations. RESULTS: Midwifery regulatory authorities and associations across Canada are consistent in establishing an expectation that midwives will provide accessible care to diverse clientele. However, how these commitments are put into practice varies considerably between jurisdictions. We compared the cases of Manitoba and Ontario to illustrate the disconnect between commitments to priority populations and implementation. DISCUSSION: While there is a clearly demonstrated intention to provide equitable access to midwifery care to all people, including "priority populations" like migrants and newcomers, in practice, these commitments have not been fully realized. Equity is encumbered by broader structural issues, such as the growth in the number of newcomers without access to public health insurance. Moves toward equity within midwifery and healthcare more broadly need to meaningfully engage with other policy sectors, such as immigration, to be able to adapt to emerging issues affecting reproductive care, such as the growing precarity of newcomer populations in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".