Community As Client: A Qualitative Descriptive Study of the Work of Midwives to Increase Access to Midwifery Care
Bibliographic record
Abstract
Increasing access to midwifery care for disadvantaged groups was an explicit goal of the regulation of midwifery in Ontario. However, people of low socio-economic status (SES) remain less likely to receive midwifery care. We conducted a qualitative descriptive study to explore the work midwives do to make midwifery care accessible to people of low SES. We interviewed 13 Ontario midwives serving people of low SES, who practiced midwifery in settings ranging from a remote solo practice to a large urban practice. Participants described a broad range of ways in which they work to enhance the approachability, acceptability, availability and accommodation, affordability, and appropriateness of their services for people of low SES. We identified two distinct approaches to increasing access to care: (1) working to maximize the existing beneficial aspects of the midwifery model to whoever presents to care, and (2) stepping outside of the confines of the midwifery model, to provide what we call “community-centred care,” in which midwives are both a part of and responsive to the broader communities that they serve. The intentional, pro-active approach used by midwives providing community-centred care could be implemented more broadly to improve access to midwifery care for people of low SES. 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".