Measuring Wellness Through Indigenous Partnerships: A Scoping Review
Bibliographic record
Abstract
Indigenous wellness has been defined in varying contexts by diverse Indigenous Peoples. The existing indicators used to measure wellness are often defined from a Western perspective. Despite the rich conceptualizations of Indigenous wellness, there exists a notable gap in how it can be measured in contemporary contexts through an Indigenous lens. A scoping review methodology with the aim of identifying measures of wellness developed through Indigenous partnerships was carried out. We completed a systematic search in the following electronic databases: PubMed, CINAHL, Psych Info Academic Search Complete, SocIndex, and the Native Health Database. We then carried out a two-stage article screening process to identify eighteen relevant papers. Content analysis was then used to identify (1) the major categories for the partnership contexts utilized in the process for measuring Indigenous wellness and (2) the kinds of measures developed. Five main categories were characterized, including the following: (1) building relationships that uphold Indigenous worldviews is important, (2) a call for co-development protocols that weave multiple worldviews, (3) the need to increase awareness of the limitations in measuring Indigenous wellness, (4) community-specific context is important, and (5) a call for strengths-based indicators. Governments, organizations, and research partners are called upon to support the co-development of meaningful engagement protocols that privilege and reflect Indigenous voices and perspectives when measuring Indigenous wellness.
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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 | 0.046 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.029 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".