Exploring Midwives’ Experiences Within Canada's First Alongside Midwifery Unit: Impacts and Implications for Midwifery Practice
Bibliographic record
Abstract
INTRODUCTION: Although midwifery-led units in hospitals are associated with positive outcomes, little is known about the experiences of the midwives who work within this model. Despite the increase in midwifery-led units globally, the first unit of this kind opened its doors in Canada in 2018. The Alongside Midwifery Unit (AMU) is staffed by a hospitalist midwife (a novel role in this country) and community midwives, working in a caseload model, who attend their clients' labor and birth on the unit. The AMU is a low-risk birthing unit located adjacent to the obstetric unit, offering midwifery-led care, in a homelike setting. Our aim was to explore and describe the experiences of midwives working in this model of care on the AMU. METHODS: Qualitative semistructured interviews and one focus group with community and hospitalist midwives working at the AMU were conducted and analyzed using a grounded theory approach. RESULTS: We identified that midwives were able to maintain the midwifery philosophy of care, strengthen relationships, amplify hospital integration, and grow midwifery leadership in this model. DISCUSSION: Implementation of an AMU supports best practice, intra- and interprofessional relationships, and integration of midwives. Our findings demonstrate a positive impact of this model along with the absence of detrimental impact on midwifery values and philosophy. An improved understanding of the impact of the AMU on midwives and their practice is useful for refining the model of care and informing implementation in other settings. This research contributes to the growing evidence demonstrating the benefits of midwifery-led units.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".