First Nations women’s experiences of out-of-hospital childbirth: Insights for enhancing paramedic practice – A scoping review
Bibliographic record
Abstract
BACKGROUND: Birthing on Country principles in Australia have seen a revitalisation in midwifery care over the last decade with it being seen as a metaphor for the best start to life for First Nations peoples. This scoping review aimed to explore the extent of evidence of Australian First Nations women's experiences of out-of-hospital childbirth and the alignment with Birthing on Country principles to inform paramedic practice. METHODS: Four databases were searched including MEDLINE, CINAHL, EBSCOhost Health and Scopus utilising the Joanna Briggs Institute (JBI) methodology for Scoping Reviews. Inclusion and exclusion criteria were identified. All articles were reviewed in a two stage process. RESULTS: Fifty two papers were yielded with 6 meeting the inclusion criteria. Using reflective thematic analysis four key themes were generated; Birthing on Country and identity, inequitable access to healthcare, trusting relationships and medicalisation of birth. CONCLUSIONS: There is a large gap in the literature surrounding delivery of care by paramedics to First Nations women birthing out-of-hospital in Australia. This review proposes supports and actions required to implement Birthing on Country principles into paramedicine. Further, standard maternity care has been found to be insufficient for First Nations women due to a lack of culturally safe care.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".