Stronger connections for better outcomes: Exploring the views and experiences of midwives working in a culturally tailored caseload midwifery model for women having a First Nations baby in Australia
Bibliographic record
Abstract
BACKGROUND: Culturally safe maternity care is a key strategy to reduce health inequities for Australian First Nations mothers and babies. Midwifery-led continuity of care (i.e., caseload) is associated with improved perinatal and psychosocial outcomes compared to other models and has been shown to improve women's experiences of care. Little is known about midwives' views and experiences of working in these models. AIM: We aimed to explore midwives' experiences of providing care in a new culturally tailored caseload model for women having a First Nations baby at three tertiary maternity services in Melbourne, Australia. METHODS: Using a descriptive qualitative design, 20 semi-structured interviews were conducted with midwives working in the new model and analysed thematically. FINDINGS: The global theme 'Stronger connections for better outcomes' comprised four sub-themes: Strengthened connections between woman and midwife; Strengthened connections to navigate systems and services; Strong connections amongst caseload midwives; and Strong connections and sustainability require responsive systems and management. DISCUSSION: The connections between women and midwives provided a greater understanding of culture and the context of women's lives, and the model facilitated co-ordination and navigation of complex services. The strong connections were jeopardised when the organisational support and resources required to provide care in the model was not provided. CONCLUSION: Midwives reflected positively about working in the model that they believed made a difference for First Nations families. Sustaining midwives in this model requires cultural training and support, a caseload that accommodates clinical and psychosocial contexts of the women, and organisational commitment and support.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.013 |
| 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".