How has the integration of midwives into primary healthcare settings impacted access to care? A qualitative descriptive study from Ontario, Canada
Bibliographic record
Abstract
PROBLEM: Most primary health care settings in Canada do not offer midwifery care. Midwifery remains poorly understood in Canada by some members of the public and healthcare providers. BACKGROUND: Most midwives in Canada work in community-based midwifery-led continuity of care models that are not integrated into interprofessional primary healthcare settings. AIM: To investigate perceptions of how integrating midwives into primary health care teams impacts access to care. METHODS: We conducted a qualitative descriptive study of expanded midwifery care models in Ontario, Canada. We completed 28 semi-structured interviews with midwives, other healthcare providers, healthcare administrators and policy makers. Interviews were audio recorded, transcribed, and then coded using open coding followed by axial coding in NVivo. We used Levesque et al.'s (Int J Equity Health 12:18, 2013) conceptualization of access to care to inform the interview questions and organize our findings. FINDINGS: We identified themes related to each of Levesque et al.'s supply side dimensions of access to care. Integrating midwives increased visibility and trust of the profession (approachability and acceptability), decreased access barriers such as travel time and cost (affordability), increased collaboration between healthcare providers (appropriateness), and ensured more timely and available care (availability and accommodation). DISCUSSION: Integrating midwives into primary healthcare settings can improve access to care, particularly for groups underserved by midwives. Integrating midwifery-led care within interprofessional teams can also enhance care appropriateness for equity-deserving populations. CONCLUSION: While stand-alone community-based midwifery care remains effective and efficient, policy makers should consider creating or expanding funding that supports the further integration of midwives into primary healthcare teams.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 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".