Djäkamirr: Exploring principles used in piloting the training of First Nations doulas in a remote multilingual Northern Australian community setting
Bibliographic record
Abstract
PROBLEM AND BACKGROUND: There is growing evidence in First Nations doula care as a strategy to address perinatal inequities and improve maternal care experiences. However, there is no evidence around the approach and principals required to successfully deliver First Nations doula (childbirth) training. QUESTION/AIM: To explore and describe the approach and principles used in piloting the training of First Nations doulas in remote, multilingual Northern Australian community settings. METHODS: Case study with participant interviews to identify principles underpinning our Decolonising Participatory Action Research (D-PAR) approach and training delivery. FINDINGS: Reflections on our D-PAR research process identified enabling principles: 1) Use of metaphors for knowledge reflexivity, 2) Accommodate cultural constructions of time 3) Practice mental agility at the Cultural Interface, 4) Advocate and address inequities, 5) Prioritise meaningful curriculums and resources, 6) Establish cross-cultural recognition and validity; and 7) Ensure continuity of First Nations culture and language. DISCUSSION: The success of our doula training pilot disrupts a pervasive colonial narrative of First Nation deficit and demonstrates that respectful, genuine, and authentic partnerships can power transformative individual and collective community change. Our D-PAR approach assumes mutual learning and expertise between community and researchers. It is well suited to collaborative design and delivery of First Nations reproductive health training.
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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 | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".