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Record W4318332946 · doi:10.1186/s12884-022-05277-8

Developing and evaluating Birthing on Country services for First Nations Australians: the Building On Our Strengths (BOOSt) prospective mixed methods birth cohort study protocol

2023· article· en· W4318332946 on OpenAlexaboutno aff
Penny Haora, Yvette Roe, Sophie Hickey, Yu Gao, Carmel Nelson, Jyai Allen, Melanie Briggs, Faye Worner, Sue Kruske, Kristie Watego, Sarah-Jade Maidment, Donna Hartz, Juanita Sherwood, Lesley Barclay, Sally Tracy, Mark Tracy, Liz Wilkes, Roianne West, Nerida Grant, Sue Kildea

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
FundersNational Health and Medical Research Council
KeywordsReproductive medicineMedicineDeveloping countryCohort studyProtocol (science)Developed countryProspective cohort studyFamily medicinePregnancyEconomic growthEnvironmental healthPopulationAlternative medicineSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: With the impact of over two centuries of colonisation in Australia, First Nations families experience a disproportionate burden of adverse pregnancy and birthing outcomes. First Nations mothers are 3-5 times more likely than other mothers to experience maternal mortality; babies are 2-3 times more likely to be born preterm, low birth weight or not to survive their first year. 'Birthing on Country' incorporates a multiplicity of interpretations but conveys a resumption of maternity services in First Nations Communities with Community governance for the best start to life. Redesigned services offer women and families integrated, holistic care, including carer continuity from primary through tertiary services; services coordination and quality care including safe and supportive spaces. The overall aim of Building On Our Strengths (BOOSt) is to facilitate and assess Birthing on Country expansion into two settings - urban and rural; with scale-up to include First Nations-operated birth centres. This study will build on our team's earlier work - a Birthing on Country service established and evaluated in an urban setting, that reported significant perinatal (and organisational) benefits, including a 37% reduction in preterm births, among other improvements. METHODS: Using community-based, participatory action research, we will collaborate to develop, implement and evaluate new Birthing on Country care models. We will conduct a mixed-methods, prospective birth cohort study in two settings, comparing outcomes for women having First Nations babies with historical controls. Our analysis of feasibility, acceptability, clinical and cultural safety, effectiveness and cost, will use data including (i) women's experiences collected through longitudinal surveys (three timepoints) and yarning interviews; (ii) clinical records; (iii) staff and stakeholder views and experiences; (iv) field notes and meeting minutes; and (v) costs data. The study includes a process, impact and outcome evaluation of this complex health services innovation. DISCUSSION: Birthing on Country applies First Nations governance and cultural safety strategies to support optimum maternal, infant, and family health and wellbeing. Women's experiences, perinatal outcomes, costs and other operational implications will be reported for Communities, service providers, policy advisors, and for future scale-up. TRIAL REGISTRATION: Australia & New Zealand Clinical Trial Registry # ACTRN12620000874910 (2 September 2020).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.095
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.095
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.066
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0040.003
Science and technology studies0.0060.002
Scholarly communication0.0050.003
Open science0.0050.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0240.008

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.

Opus teacher head0.048
GPT teacher head0.439
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

Explore more

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