Measuring health and well-being from preconception to early life in Indigenous populations: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Indigenous Peoples face a notable absence of standardized equity indicators for non-communicable diseases. Existing measurement models are rooted in Euro-Western biomedical frameworks that overlook Indigenous worldviews, definitions of health, and relational approaches to wellness. These models remain narrow in scope-focused on disease-specific indicators and treatment pathways-and reinforce deficit-oriented perspectives rather than advancing holistic, strengths-based understandings of well-being. This scoping review aims to identify and synthesize research that utilizes, assesses, or validates measures of wellness, health, and supportive early environments in Indigenous populations. METHODS: This review will use an Indigenous-informed scoping review study methodology, which grounds each stage of the review process in Indigenous values and community guidance. A systematic search of global academic and grey literature databases will be conducted to identify relevant literature. Selected studies will include those that assess or validate the measurement of health and wellness spanning from preconception through pregnancy, infancy, and early childhood within Indigenous populations in Canada, Australia, New Zealand, and the USA. Articles will be screened and assessed for eligibility by two reviewers. From eligible articles, data including author and year of publication; source country; target population; objectives; name(s) of instrument(s); type(s) of measure(s); development/adaptation/validation process; main outcomes; community engagement; quality assessment; and other descriptive variables will be extracted. A thematic analysis approach guided by an Indigenous Community Advisory Committee will be applied to synthesize the findings. DISCUSSION: This scoping review aims to identify and synthesize the global literature on tools and instruments to measure health, well-being, and supportive early environments in Indigenous populations. This work aims to inform the development of Indigenous wellness indicators for the Indigenous Healthy Life Trajectories Initiative (I-HeLTI) Cohort Research Study funded by the Canadian Institutes of Health Research. Traditional population health monitoring methods, rooted in Western paradigms, have often perpetuated colonial biases and overlook unique contexts of Indigenous communities. This review seeks to bridge knowledge gaps in developing and validating Indigenous wellness indicators that align with Indigenous values and aspirations. The findings are expected to advance ethical approaches to health measurement in Indigenous populations, supporting data sovereignty and culturally inclusive wellness indicators. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework https://osf.io/yfv8m .
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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 | 0.143 | 0.118 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
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".