Translating evidence into practice: Implementing culturally safe continuity of midwifery care for First Nations women in three maternity services in Victoria, Australia
Bibliographic record
Abstract
Background: Strategies to improve outcomes for Australian First Nations mothers and babies are urgently needed. Caseload midwifery, where women have midwife-led continuity throughout pregnancy, labour, birth and the early postnatal period, is associated with substantially better perinatal health outcomes, but few First Nations women receive it. We assessed the capacity of four maternity services in Victoria, Australia, to implement, embed, and sustain a culturally responsive caseload midwifery service. Methods: A prospective, non-randomised research translational study design was used. Site specific culturally responsive caseload models were developed by site working groups in partnership with their First Nations health units and the Victorian Aboriginal Community Controlled Health Organisation. The primary outcome was to increase the proportion of women having a First Nations baby proactively offered and receiving caseload midwifery as measured before and after programme implementation. The study was conducted in Melbourne, Australia. Data collection commenced at the Royal Women's Hospital on 06/03/2017, Joan Kirner Women's and Children's Hospital 01/10/2017 and Mercy Hospital for Women 16/04/2018, with data collection completed at all sites on 31/12/2020. Findings: The model was successfully implemented in three major metropolitan maternity services between 2017 and 2020. Prior to this, over a similar timeframe, only 5.8% of First Nations women (n = 34) had ever received caseload midwifery at the three sites combined. Of 844 women offered the model, 90% (n = 758) accepted it, of whom 89% (n = 663) received it. Another 40 women received standard caseload. Factors including ongoing staffing crises, prevented the fourth site, in regional Victoria, implementing the model. Interpretation: Key enablers included co-design of the study and programme implementation with First Nations people, staff cultural competency training, identification of First Nations women (and babies), and regular engagement between caseload midwives and First Nations hospital and community teams. Further work should include a focus on addressing cultural and workforce barriers to implementation of culturally responsive caseload midwifery in regional areas. Funding: Partnership Grant (# 1110640), Australian National Health and Medical Research Council and La Trobe University.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".