What is known about Indigenous women’s dissatisfaction of Birthing experiences in mainstream maternity hospitals in Australia, Aotearoa, Canada, US, Kalaallit Nunaat and Sápmi? A systematic scoping review
Bibliographic record
Abstract
Introduction: Understanding Indigenous women's dissatisfaction with birthing experiences is vital for improving maternal healthcare. It highlights the need for compassionate, respectful care that meets women's physical and emotional needs. Addressing these concerns can enhance patient satisfaction, reduce postpartum mental health issues and wellness, and ensure safer, more positive outcomes for mothers and babies. Objectives: This scoping review aimed to identify what is known about Indigenous women's dissatisfaction of birthing experiences in mainstream maternity hospitals. Inclusion criteria: This review considered primary research studies that reported on reasons for dissatisfaction of birthing experiences, and strategies implemented to improve quality of clinical practice around women's dissatisfaction of birthing experiences in mainstream maternity hospitals in Australia, Aotearoa, Canada, US, Kalaallit Nunaat and Sápmi. Findings: A total of 22 manuscripts reporting 22 studies met the inclusion criteria and were included in the synthesis. Discussion: There is a need for culturally safe trauma informed care, inclusive communication, active decision-making involvement and greater inclusion of Indigenous perspectives in maternity care, including the involvement of Indigenous birth support workers where appropriate and inclusion of Birthing on Country models of care. Conclusion: This review reveals that the medicalisation and evacuation of Indigenous women for childbirth cause cultural, geographic, and social disconnection, despite infant safety benefits. It underscores the need for better cultural safety education, communication, and the inclusion of cultural practices in care, with support from Indigenous birth support workers being essential.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".