Toward Equity in Access to Midwifery: A Scan of Five Canadian Provinces
Bibliographic record
Abstract
This research project was created to support equitable access to midwifery care for the diverse populations of Saskatchewan women. Given the ongoing implementation and expansion of midwifery across diverse mixes of rural, urban, and aboriginal communities in the health regions of the province, we asked: How can midwifery care be implemented in an equitable and accessible way in Saskatchewan? The first phase of this research explored experiences with midwifery implementation around issues of accessibility through an environmental scan of five Canadian provinces (British Columbia, Manitoba, Ontario, Northwest Territories, and Nova Scotia). By analyzing policy and regulatory documents together with primary data generated through key informant interviews, we discovered an interesting compendium of provincial activities and policies in support of equity to access midwifery. We also identified several important areas in need of strengthening. In this article, we present a brief description of the best practices identified by each province, followed by an exploratory analysis of key thematic issues that are significant in creating equitable access to the full scope of midwifery care. These included funding models, interprofessional relationships, choice of birthplace and second attendants, risk designation, geographic dispersal, community integration, and midwifery human resources.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".