‘Walking the journey’ with pregnant and birthing women from remote Australian First Nations communities: A qualitative study in the Top End of the Northern Territory
Bibliographic record
Abstract
PROBLEM/BACKGROUND: Australian First Nations people experience disproportionate burdens of poor outcomes compared to non-First Nations people. Further, women living in remote communities face more barriers to care-seeking in pregnancy. Despite work being done in some remote communities, there is limited data exploring women's experiences of pregnancy care, thus a limited understanding of specific barriers and enablers to care-seeking for these women. AIM: This study aimed to identify barriers and enablers to care-seeking during pregnancy for Australian First Nations women living in several remote communities in the Northern Territory, by listening to their stories. METHODS: Yarning, highly regarded and rigorous qualitative approach developed by and for First Nations peoples, was undertaken in several settings with women living in remote First Nations communities. Using purposive sampling, nine women participated. FINDINGS: Two themes emerged: (1) the importance of family and community for women's emotional wellbeing; (2). ways healthcare providers and services build trust with pregnant women. DISCUSSION: Women identified various family and community members as significant sources of support in community and while hospitalised, including having companions while away from home. Further, reduced access to community life impacted emotional wellbeing. Continuity-of-care throughout pregnancy was essential for building trust, as was responsive, clear communication. Intentional connection building by care providers enabled development of trust. CONCLUSION: Providing culturally safe care will likely facilitate enablers and reduce barriers to care-seeking in pregnancy in remote communities. It requires ongoing and sustained efforts to ensure true partnership and collaboration between First Nations peoples and health services.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".