An Early Feasibility Study of Midwifery Services in a Vulnerable Population
Bibliographic record
Abstract
Objectives: Canadian women and newborns are usually healthy due to the availability of prenatal care, postnatal care, and the presence of a skilled health professional. However, social determinants of health can have a significant impact on women’s ability to access high-quality care, particularly during pregnancy. We partnered with Aspen, a not-for-profit social service organization in Calgary, to explore the feasibility of implementing midwifery services for a vulnerable population. Methods: We conducted interviews with Aspen clients, Calgary registered midwives, and focus groups with Aspen staff to understand their perceptions of midwifery services, including benefits and potential barriers to their implementation. We used administrative data to develop a demographic profile of Aspen clients. Results: Our results suggest that midwives would be acceptable birth providers, but this further depends on women’s culture and their previous pregnancy experience. The study highlighted key aspects that should be considered to successfully implement midwifery services for the vulnerable population, including public awareness about midwifery services, access to an interprofessional team, and allocation of additional funding to practicing midwives. Conclusion: Midwifery care would be an acceptable and perhaps more-appropriate maternity care model for vulnerable populations. This article has been peer reviewed.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".