Xpey’ Relational Environments: an analytic framework for conceptualizing Indigenous health equity
Bibliographic record
Abstract
INTRODUCTION: Both health equity research and Indigenous health research are driven by the goal of promoting equitable health outcomes among marginalized and underserved populations. However, the two fields often operate independently, without collaboration. As a result, Indigenous populations are underrepresented in health equity research relative to the disproportionate burden of health inequities they experience. In this methodological article, we present Xpey' Relational Environments, an analytic framework that maps some of the barriers and facilitators to health equity for Indigenous peoples. METHODS: Health equity research needs to include a focus on Indigenous populations and Indigenized methodologies, a shift that could fill gaps in knowledge with the potential to contribute to 'closing the gap' in Indigenous health. With this in mind, the Equity Lens in Public Health (ELPH) research program adopted the Xpey' Relational Environments framework to add a focus on Indigenous populations to our research on the prioritization and implementation of health equity. The analytic framework introduced an Indigenized health equity lens to our methodology, which facilitated the identification of social, structural and systemic determinants of Indigenous health. To test the framework, we conducted a pilot case study of one of British Columbia's regional health authorities, which included a review of core policies and plans as well as interviews and focus groups with frontline staff, managers and senior executives. CONCLUSION: ELPH's application of Xpey' Relational Environments serves as an example of the analytic framework's utility for exploring and conceptualizing Indigenous health equity in BC's public health system. Future applications of the framework should be embedded in Indigenous research methodologies.
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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 | 0.022 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".