Making Space for Midwifery in a Hospital: Exploring the Built Birth Environment of Canada’s First Alongside Midwifery Unit
Bibliographic record
Abstract
BACKGROUND: Canada's first alongside midwifery unit (AMU) was intentionally informed by evidence-based birth environment design principals, building on the growing evidence that the built environment can shape experiences, satisfaction, and birth outcomes. OBJECTIVES: To assess the impact of the built environment of the AMU for both service users and midwives. This study aimed to explore the meanings that individuals attribute to the built environment and how the built environment impacted people's experiences. METHODS: We conducted a mixed-methods study using a grounded theory methodology for data collection and analysis. Our research question and data collection tools were underpinned by a sociospatial conceptual approach. All midwives and all those who received midwifery care at the unit were eligible to participate. Data were collected through a structured online survey, interviews, and focus group. RESULTS: Fifty-nine participants completed the survey, and interviews or focus group were completed with 28 service users and 14 midwives. Our findings demonstrate high levels of satisfaction with the birth environment. We developed a theoretical model, where "making space" for midwifery in the hospital contributed to positive birth experiences and overall satisfaction with the built environment. The core elements of this model include creating domestic space in an institutional setting, shifting the technological approach, and shared ownership of the unit. CONCLUSIONS: Our model for creating, shifting, and sharing as a way to make space for midwifery can serve as a template for how intentional design can be used to promote favorable outcomes and user satisfaction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| 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".