Developing Responsive Indicators of Indigenous Community Health
Bibliographic record
Abstract
How health is defined and assessed is a priority concern for Indigenous peoples due to considerable health risks faced from environmental impacts to homelands, and because what is "at risk" is often determined without their input or approval. Many health assessments by government agencies, industry, and researchers from outside the communities fail to include Indigenous definitions of health and omit basic methodological guidance on how to evaluate Indigenous health, thus compromising the quality and consistency of results. Native Coast Salish communities (Washington State, USA) developed and pilot-tested a set of Indigenous Health Indicators (IHI) that reflect non-physiological aspects of health (community connection, natural resources security, cultural use, education, self-determination, resilience) on a community scale, using constructed measures that allow for concerns and priorities to be clearly articulated without releasing proprietary knowledge. Based on initial results from pilot-tests of the IHI with the Swinomish Indian Tribal Community (Washington State, USA), we argue that incorporation of IHIs into health assessments will provide a more comprehensive understanding of Indigenous health concerns, and assist Indigenous peoples to control their own health evaluations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".