Preconception Health of Indigenous Peoples in Australia, Canada, New Zealand, and the United States: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the importance of the preconception period for addressing reproductive and intergenerational health inequities and supporting improved maternal and child health outcomes. This study aimed to understand the extent and type of evidence that exists in relation to preconception health for Indigenous peoples living in high-income countries with similar experiences of colonisation, namely, Australia, New Zealand, Canada, and the United States. METHODS: This review was conducted as per the JBI methodology and PRISMA Extension for Scoping Reviews. A comprehensive search of PubMed, CINAHL [EBSCO], Ovid Embase, Scopus, and the Wiley Cochrane Library was conducted using keywords and index terms. We included research in English published between January 2010 and June 2023 on quantitative and qualitative primary studies. Data were extracted using a standardised tool, and the analysis included quantitative descriptions and qualitative content analysis. RESULTS: We identified 360 potential studies and included 57 articles in the review. Most studies were from the United States (n = 36, 63.2%) and Australia (n = 13, 22.8%), and they commonly reported associations between preconception health risk factors and maternal or child health outcomes (n = 27, 48.2%) or described the development, implementation, or evaluation of preconception health interventions (n = 26, 46.4%). Common preconception health areas were pre-pregnancy body mass index or weight (n = 34), alcohol (n = 16), diet (n = 14), physical activity (n = 12), and diabetes (n = 11). Most studies focused exclusively on women (n = 46, 80.7%), and very few included men (n = 3, 5.3%). The study populations were mostly urban and rural (n = 25, 43.9%) or rural only (n = 14, 24.6%); however, the geographical remoteness was often unclear (n = 14, 24.6%). CONCLUSIONS: While there was some research relating to the preconception health of Indigenous peoples, this review identified considerable research gaps. There is a need for dedicated research into preconception health risk factors and reproductive health outcomes, attitudes and awareness of preconception health, and preconception health interventions for Indigenous peoples.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".